The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data.

Urban land surface temperature (LST) plays a crucial role in observing and comprehending energy exchange within urban environments. Urban geometric structure and material composition are critical parameters to characterize urban thermal radiation accurately. In this paper, an urban thermal radiation analytical model based on the sky view factor (UTRAM-SVF) was developed by considering the multiple scattering within the urban canopy and the radiation composition of urban components. The cross-comparison of the proposed method was conducted in four ways: the Discrete Anisotropic Radiation Transfer (DART) model, urban effective emissivity model based on SVF (UEM-SVF), Landsat 8 Thermal Infrared Sensor (TIRS) data, and Airborne Hyperspectral Scanner (AHS) TIR data. Compared with DART, the results revealed that the root mean square error (RMSE) of radiance by the UTRAM-SVF model are 0.04 W/ (m ² ∙sr∙μm). Furthermore, two field applications were conducted using Landsat 8 TIRS data and AHS TIR data. The differences of at-sensor radiance from UTRAM-SVF model and Landsat 8 TIRS data vary from 0.05 to 0.3 W/ (m ² ·sr·μm), and the RMSE is 0.17 W/ (m ² ·sr·μm). The results of AHS TIR images on UTRAM-SVF model present that the average biases of at-sensor radiance are 0.07 and 0.19 W/ (m ² ·sr·μm) for two TIR channels, respectively. The cross-comparison results show that the proposed method outperformed the UEM-SVF model in evaluating radiance over the complex and heterogenous urban areas (SVF< 0.4). The UTRAM-SVF model can be used to monitor urban thermal radiation based on high-resolution TIR data, and can further be helpful to retrieve high-resolution urban LST.

Land surface emissivity (LSE) is crucial for retrieving land surface temperature (LST) from Landsat 9 TIRS-2 thermal infrared data. However, the single-band LSE product (band 10) provided officially is insufficient for split-window (SW) algorithm requiring dual-band emissivity inputs. This letter proposes a land cover and channel transformed (LCCT-LSE) method to estimate band 11 LSE and enables LST retrieval using SW algorithm on Google Earth Engine. Cross-validation with MOD21 LSE products showed that the LCCT-LSE method achieved a mean absolute error (MAE) of 0.004 and a root mean square error (RMSE) of 0.005, outperforming classification-based method, NDVI threshold method, and vegetation cover (VCM) methods. In situ validation showed SW-retrieved LST attains MAE/RMSE of 1.27 K/ 2.13 K, with consistent accuracy across diverse land covers (water: 0.86 K, soil: 1.58 K, desert: 1.71 K, sand: 1.80 K, vegetation: 0.87 K). A comparison with the official Landsat 9 LST product indicated that the bias of retrieved LST is within 1K for all land cover classes (cropland, forest, grassland, shrubland, water, barren and impervious) in Beijing. These results demonstrated that the LCCT-LSE method is capable of estimating the LSE in Landsat 9 band 11 with a reliable and accurate result. This study provides a new insight for LST retrieval from Landsat 9 data.

The role of land surface temperature (LST) is of utmost importance in multiple academic disciplines such as climatology, hydrology, ecology, and meteorology. Until to now, many methods have been proposed to estimate LST from satellite thermal infrared data. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12μm, affected strongly by both atmospheric water vapor and ozone. Three study areas, namely Baotou, Heihe River Basin, and Yantai-sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted by cross-comparison between the retrieved LST and MODIS LST product. The results revealed that the WVS-based method exhibited an average bias of 0.63K and an RMSE of 1.62K compared to the in-situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77K and RMSE of 2.0K. These findings provide that the WVS-based method is effective to estimate LST from ZY1-02E IRS data.

Satellite thermal infrared remote sensing observations primarily retrieve nadir-view radiometric surface temperature (Tr), which underrepresents vertical walls and vegetation-building thermal interactions in three-dimensional urban environments. This study developed a vegetation-enhanced semi-empirical model (Tc-VEM) for estimating complete urban surface temperature (Tc) by replacing geometric parameters with sky view factor and incorporating leaf area index (LAI). The model was calibrated using coupled TUF-Ped and SUM models and validated against UAV observations in a residential area of Nanjing. Simulation results show that Tc-VEM achieved daytime and nighttime R² values of 0.991 and 0.998 and RMSEs of 0.91 and 0.32 °C, respectively, with UAV validation accuracy of approximately 0.8 °C. Component-temperature analysis showed that increasing LAI from 0.0 to 3.9 reduced mean daytime ground temperature by 5.66 °C and wall temperatures by 2.52–4.86 °C, but roof temperature by only 0.12 °C. Vegetation preferentially cooled nadir-visible ground, causing Tr to decrease faster than Tc and shifting Tc−Tr toward positive values as LAI increased. Mean ΔT remained negative across built LCZs in all seasonal observations, while sensitivity experiments showed positive shifts at high LAI. Intra-LCZ standard deviations were lowest in spring and generally highest in summer or autumn, revealing seasonally varying within-class heterogeneity. The systematically higher Tr-based than Tc-based surface urban heat island intensity (SUHII) indicates that nadir observations can overestimate urban–rural thermal contrasts when vegetation cooling is stronger in the reference area. This paper provides a methodological framework for assessing a new type of urban heat island.