Evaluating Daytime and Nighttime Land Surface Temperature Pattern and Trends in India: A Comparative Analysis of Satellite and Reanalysis Data

Land surface temperature (LST) is a highly dynamic and key variable of the Earth’s energy budget, which makes it crucial to understand its behavior from global to regional scales. It can be examined across extensive geographic regions using satellite or reanalysis datasets, offering high spatial and temporal resolutions with global coverage. Thus, the present study aims to analyze the comparative spatial distribution and trends in the daytime and nighttime mean LST over the six climatic regions of India for the period 2003–2022 using satellite (MODIS and AIRS Aqua) and reanalysis (ERA5-Land and MERRA-2) datasets. The mean LST of satellites and reanalysis datasets are juxtaposed using various statistical methods, including the correlation coefficient (r), root mean square error (RMSE), percentage bias (Pbias), and mean absolute error (MAE). Additionally, the seasonal and annual LST trends are computed using Theil Sen’s Slope Estimator and Contextual Mann-Kendall (CMK) significance test. The findings show a very high correlation (> 0.85) in the daytime and nighttime LST of all four datasets. Furthermore, the RMSE values vary from 2.71 °C to 13.51 °C at an annual scale. However, nighttime LST is more consistent than daytime among the datasets. Across all datasets, a significant cooling trend is observed in annual daytime LST (MODIS Aqua: -0.115 °C/yr to MERRA-2: -0.004 °C/yr), except ERA5-Land (0.004 °C/yr). For annual nighttime LST, all datasets indicate warming, ranging from 0.010 °C/yr (AIRS Aqua) to 0.049 °C/yr (MODIS Aqua), except MERRA-2 (-0.003 °C/yr). Annual daytime mean LST shows a cooling trend across most of the regions, with the largest decline in the North-West (-0.070 °C/yr) and West-Central (-0.064 °C/yr). In contrast, North-East (0.009 °C/yr) and Himalayan Region (0.014 °C/yr) regions exhibit slight warming in the daytime mean LST. However, nighttime mean LST indicates warming in all the climatic regions, except the Himalayan Region (-0.003 °C/yr). Overall, the study suggests the usage of nighttime LST data over daytime LST considering its consistency. The present study aids in refining the comprehension of LST trends, bolstering the credibility of these datasets for climate studies, environmental monitoring, and policy formulation, especially in the diverse landscapes of India.