Sensitivity Analysis and Validation of Daytime and Nighttime Land Surface Temperature Retrievals from Landsat 8 Using Different Algorithms and Emissivity Models

Abstract

Land Surface Temperature (LST) is a substantial element indicating the relationship between the atmosphere and the land. This study aims to examine the efficiency of different LST algorithms, namely, Single Channel Algorithm (SCA), Mono Window Algorithm (MWA), and Radiative Transfer Equation (RTE), using both daytime and nighttime Landsat 8 data and in-situ measurements. Although many researchers conducted validation studies of daytime LST retrieved from Landsat 8 data, none of them considered nighttime LST retrieval and validation because of the lack of Land Surface Emissivity (LSE) data in the nighttime. Thus, in this paper, we propose using a daytime LSE image, whose acquisition is close to nighttime Thermal Infrared (TIR) data (the difference ranges from one day to four days), as an input in the algorithm for the nighttime LST retrieval. In addition to evaluating the three LST methods, we also investigated the effect of six Normalized Difference Vegetation Index (NDVI)-based LSE models in this study. Furthermore, sensitivity analyses were carried out for both in-situ measurements and LST methods for satellite data. Simultaneous ground-based LST measurements were collected from Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) stations, located at different rural environments of the United States. Concerning the in-situ sensitivity results, the effect on LST of the uncertainty of the downwelling and upwelling radiance was almost identical in daytime and nighttime. Instead, the uncertainty effect of the broadband emissivity in the nighttime was half of the daytime. Concerning the satellite observations, the sensitivity of the LST methods to LSE proved that the variation of the LST error was smaller than daytime. The accuracy of the LST retrieval methods for daytime Landsat 8 data varied between 2.17 K Root Mean Square Error (RMSE) and 5.47 K RMSE considering all LST methods and LSE models. MWA with two different LSE models presented the best results for the daytime. Concerning the nighttime accuracy of the LST retrieval, the RMSE value ranged from 0.94 K to 3.34 K. SCA showed the best results, but MWA and RTE also provided very high accuracy. Compared to daytime, all LST retrieval methods applied to nighttime data provided highly accurate results with the different LSE models and a lower bias with respect to in-situ measurements.

Global Land Surface Temperature Change (2003–2017) and Its Relationship with Climate Drivers: AIRS, MODIS, and ERA5-Land Based Analysis

“…Changes in surface temperature can be caused by changes in land surface conditions and climatic variables [20]. Land cover changes, e.g., afforestation and deforestation, have effects on LST via changes in the evapotranspiration, albedo, and land surface emissivity, which serves as the primary input for the retrieval of LST [21][22][23][24]. Clouds, aerosols, and greenhouse gases are all factors that affect radiative forcing and, consequently, surface temperature [25][26][27].…”

Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm

“…In-situ LST comes from the Surface Radiation Budget Network (SURFRAD). The SURFRAD network was established by the National Oceanic and Atmospheric Administration in 1993 to support climate-related research over the United States by providing long-term, continuous, and accurate in-situ surface radiation budget [75]. The system now has seven sites in different climatological regions of the United States (as shown in Fig 4).…”

Editorial for the Special Issue “Remote Sensing Monitoring of Land Surface Temperature”

“…The combination of the state-of-the-art in the thermal infrared (TIR) domain [1][2][3] with the recent advances in the capabilities provided by operating and new satellites [4][5][6][7][8][9][10], UAVbased [11] or aerial remote sensing are boosting the use of land surface temperature (LST) in a variety of research fields [5,8,9,11,12]. LST plays a key role in soil-vegetation-atmosphere processes and becomes crucial in the estimation of surface energy flux exchanges, actual evapotranspiration, or vegetation and soil properties [8,9].…”

“…The latest advances in data fusion, downscaling and disaggregation techniques provide a new dimension to LST applications in water resource and agronomic management thanks to the improvement in both the temporal and spatial resolution of thermal products [8][9][10]. However, at the same time, continuous research into LST estimation algorithms, as well as continuous calibration and validation, are still required to improve the accuracy of ground LST data and satellite LST products [1][2][3][4][5]13,14].…”

“…Published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation [1][2][3][4], improving long-term consistency in satellite LST [5][6][7], downscaling LST [8][9][10], LST applications [11,12] and land surface emissivity research [13,14].…”