# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 1 Introduction  
Land surface temperature (LST) is one of the key parameters in the land surface physical process on regional and global scales, and an accurate LST is essential for the study, such as agricultural drought monitoring, climate change [1, 2], estimating air temperature and evapotranspiration [3, 4], analyzing land cover change, and urbanization [5, 6]. Traditionally, ground measurements cannot practically achieve an aim that measures the LST over wide and continuous areas. With the development of remote sensing from space, satellite remote sensing is a powerful and effective method for analyzing the spatiotemporal variation of LST over the entire globe with a sufficiently high spatial-temporal resolution. It can provide multi-temporal, multi-spectral, and real-time data. In this way, remote sensing is become more applicable and highly preferred compared to traditional ground measurements. The spatiotemporal variation of LST is important for detecting changes in land surface characteristics and understanding climate change. This study utilizes MERSSI-II sensor data, which provides a unique opportunity for monitoring the changes in LST at spatialand temporal scales. The MERSI-II sensor is carried by the Chinese FY-3D polar-orbiting meteorological satellite, which has a higher temporal and spatial resolution than the MODIS satellite, and can be utilized as a backup data source for global LST measurements. MERSI-II is equipped with two thermal infrared (TIR) channels (bands 24 and 25), and provides a spatial resolution of up to 250 m, which confirms the basic requirements for LST estimation. Until now, a few studies have been published on LST retrieval from FY-3D MERSI-II data [7; 8; 9]. The SW algorithm is categorized as a multi-channel algorithm because of its superior accuracy when compared to in situ data; the algorithm is considered one of the most mature methods for retrieving LST using TIR remote sensing, and it has a wide application range [10; 11]. LST products are generally generated for various satellite sensors using various algorithms based on the intended research aims, and the retrieval of LST utilizing TIR data has made significant development [12]. In this study, we introduced the application potential of the recently listed Two-Factor Split Window (TFSW) algorithm modified by [PERSON] et al. [7] for the MERSI-II instrument, to analyze the spatiotemporal variation of LST and validation in Pakistan for the first time. The average mean absolute error and R\({}^{2}\) of the TFSW algorithm was 1.97 K and 0.98, respectively, retrieved from MERSI-II data [7]. On a global scale, the mean temperature was 1.2 \(\pm\) 0.1 \({}^{\circ}\)C and 1.39 \({}^{\circ}\)C (in Asia); for 2020, this was above the 1850-1900 baseline data. Pakistan is highly vulnerable to climate change [13; 14]. The most prominent devastating consequences of climate change are increases of floods, storms, droughts, and heatwaves [13]. The research study conducted by [PERSON] et al. [15] for Pakistan shows a total change of a temperature warming trend up to 0.2 \({}^{\circ}\)C since the beginning of the last century by incorporating a long-term time series (1876-1993) using reconstructed temperature data. During 1901-2007, the overall increase in the area-weighted mean temperature of Pakistan was 0.64 \({}^{\circ}\)C, and it has been continuously rising at 0.06 \({}^{\circ}\)C per decade [16]. Until now, studies about LST monitoring at the country level are rare, and a few studies have been published regarding LST monitoring at small regional scales such as [PERSON] et al. [17], who examined the spatiotemporal changes in land use land cover (LULC) and investigated its effects on LST using 20 years of data and a CA-Markov model specially for the two urban cities. [PERSON] et al. [18] reported that the urban land cover of Faisalabad city in Pakistan caused higher regional temperatures according to the result of LULC changes. Similarly, [PERSON] et al.