Land use land cover (LULC) of city regions is strongly affected by urbanization and affects the thermal environment of urban centers by influencing the surface temperature of core city areas and their surroundings. These issues are addressed in the current study, which focuses on two provincial capitals in Pakistan, i.e., Lahore and Peshawar. Using Landsat data, LULC is determined with the aim to (a) examine the spatio-temporal changes in LULC over a period of 20 years from 1998 to 2018 using a CA-Markov model, (b) predict the future scenarios of LULC changes for the years 2023 and 2028, and (c) study the evolution of different LULC categories and investigate its impacts on land surface temperature (LST). The results for Peshawar city indicate the significant expansion in vegetation and built-up area replacing barren land. The vegetation cover and urban area of Peshawar have increased by 25.6%, and 16.3% respectively. In contrast, Lahore city urban land has expanded by 11.2% while vegetation cover decreased by (22.6%). These transitions between LULC classes also affect the LST in the study areas. Transformation of vegetation cover and water surface into built-up areas or barren land results in the increase in the LST. In contrast, the transformation of urban areas and barren land into vegetation cover or water results in the decrease in LST. The different LULC evolutions in Lahore and Peshawar clearly indicate their effects on the thermal environment, with an increasing LST trend in Lahore and a decrease in Peshawar. This study provides a baseline reference to urban planners and policymakers for informed decisions.

The thermal Infrared Spectrometer (TIS) is the thermal infrared (TIR) sensor on-board the first Sustainable Development Goals (SDG-1) satellite. The TIS data can potentially be used to support improved monitoring of ground conditions with high-spatial resolutions, so accurate radiometric calibration is required. A meticulous radiometric calibration was conducted on the prototype of TIS to test its ability to convert a raw digital number (DN) to at-aperture radiance. The initial maximum radiometric error was 2.19 K at 300 K for Band 1(B1) and the minimum radiometric error was 0.25 K at 300 K rooted in Band 3 (B3). The R-Squared (R²) was over 0.99 for each band. The methodology was refined to divide the channel detectable temperature range into three sub-ranges and then the maximum radiometric errors were reduced to less than 1 K at 300 K for three bands. Subsequently, the Generalized Split-Window (SW) algorithm was preformed to estimate the ability of TIS on land surface temperature (LST) retrieval. In order to take advantage of its high-spatial resolution and make full use of TIR data, three-channel SW algorithm was also performed for intercomparison. Results showed that the SW algorithm can obtain LST with root-mean-square error (RMSE) less than 1K. Compared with two-channel algorithm with RMSE = 0.94 K, three-channel algorithm achieves better results in retrieving LST with RMSE = 0.82 K. For different land surface types, water samples achieved the minimum RMSE, and for different atmospheric column water vapor (CWV), dry atmospheres obtained better results. The sensitivity analysis of SW algorithm was considered along with noise-equivalent differential temperature (NEΔT), uncertainty of land surface emissivity (LSE) and input land surface temperature ($T_s$). Generally, three-channel algorithm was more stable to LSE uncertainties, and the error changes were within 40%. But when NEΔT and $T_s$ uncertainties were included, the error percentage of three-channel SW method increases more, which means three-channel SW method is more sensitive to those two factors. All in all, the methodology and results used for radiometric calibration and LST retrieval in this study provide valuable guidance for the flight model of TIS and post-launch applications.

The authors wish to make the following corrections to this paper