**Aliihsan Sekertekin 1,\* and Stefania Bonafoni <sup>2</sup>**


Received: 19 July 2020; Accepted: 25 August 2020; Published: 26 August 2020

**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.

**Keywords:** land surface temperature (LST); daytime LST; nighttime LST; validation; land surface emissivity (LSE); single channel algorithm; radiative transfer equation; mono window algorithm; SURFRAD data; Landsat 8

#### **1. Introduction**

Land Surface Temperature (LST), also named skin temperature, refers to the surface temperature of the Earth. The International Geosphere and Biosphere Program (IGBP) [1] accepted the LST as one of the high-priority parameters, and the Global Climate Observing System (GCOS) [2] identified it as an Essential Climate Variable (ECV). Considering space-borne, airborne, and ground-based remote sensors, LST represents the accumulative radiometric surface temperature of all materials of the surface cover covering the sensor's field of view in the observation direction [3]. Thus, LST estimation from Thermal Infrared (TIR) images is a complicated procedure since the Earth's surface is composed of dissimilar materials of varying geometry [4–6]. For example, the LST pixel of a densely vegetated area represents the surface temperature of vegetation; however, for a sparsely vegetated area, the surface temperature of vegetation and soil together comprises the LST of the area [5].

LST is a crucial parameter for many fields of interest such as surface energy and water balance, ecology, agriculture, environment, climatology, meteorology, and hydrology [7–9]. Thus, it provides an improved understanding of a wide range of applications involving drought monitoring [10–12], Surface Heat Island (SHI) and urban climate studies [13–17], surface soil moisture and evapotranspiration estimation [18,19], numerical weather prediction and data assimilation [20,21], surface turbulent flux estimation [22], monitoring of heat waves [23], earthquake prediction [24,25], forest fire monitoring [26], and monitoring of geothermal activities [27,28].

The history of satellite-derived LST goes back to TIROS-II satellite, which was launched at the beginning of the 1960s [29,30]. Through meteorological stations, surface temperature estimation from radiance measurements is a classical point-based technique; nevertheless, this technique does not stand for the LST on a large scale. To overcome this drawback, spaceborne TIR remote sensing has been extensively examined for LST retrieval, and regional and global scale monitoring is the main advantage of this technology. However, surface parameters (emissivity and geometry), sensor parameters (spectral range and viewing angle), and atmospheric effects are the major factors that influence the accuracy of the LST retrieval from TIR data of satellites [5,29,31–33]. Thus, accurate estimation of Land Surface Emissivity (LSE) and atmospheric parameters is a crucial procedure to obtain LST from TIR data [34]. Concerning these parameters, various TIR-based multi-channel and single-channel LST retrieval methods have been proposed by the researchers for different sensor types. Namely, these are Temperature-Independent Spectral Indices (TISI) method [35], Split Window Algorithm (SWA) [36–38], Mono Window Algorithm (MWA) [39], Single Channel Algorithm (SCA) [40,41], Radiative Transfer Equation (RTE) [42,43], and Temperature and Emissivity Separation (TES) method [44]. Among the LST retrieval methods above, only SWAs do not need atmospheric parameters such as water vapor profile and/or temperature. The LSE and LST errors arising from the other algorithms largely rely on the input atmospheric profile's uncertainties [45].

There are numerous Earth observation sensors, namely, Geostationary Operational Environmental Satellite (GOES), Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Along-Track Scanning Radiometer (AATSR), The Spinning Enhanced Visible and Infrared Imager (SEVIRI), The Advanced Very High Resolution Radiometer (AVHRR), The Visible Infrared Imaging Radiometer Suite (VIIRS), and Sentinel-3, providing operational daytime and nighttime LST products with low spatial resolution (from 750 m to 4 km). However, TIR data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat satellite series have higher spatial resolution but lower temporal resolution than the sensors reported above. Regarding these limitations, LST retrieval having both high temporal and spatial resolution is a challenge for thermal remote sensing studies. However, LST images obtained from Landsat and ASTER TIR data are unique sources to investigate the thermal environment of cities and their surroundings due to the higher spatial resolution in TIR bands. Moreover, Landsat-derived LST is one of the most commonly preferred data for various applications stated above.

The demand for satellite-based LST products has been increasing rapidly. Thus, the quality of the LST data used in the studies should be examined by a validation procedure for accurate and reliable analyses. Validation provides information about the quantitative uncertainty, enabling the proper use and application of the product. Thus, any algorithm or product would not be broadly welcomed without performing thorough calibration and validation [46]. Overall, cross-validation, the Temperature-based method (T-based) and the Radiance-based method (R-based) are three main techniques considered to evaluate space-based LST [31,34]. Many researchers have considered one or two of these methods for satellite-based LST validation derived from Landsat missions [34,47–55], Sentinel-3A [56], GOES [57], SEVIRI [58,59], MODIS [60–62], AATSR [58,62,63], VIIRS [64], ASTER [65,66] and AVHRR [38]. In this work, we utilized the T-based technique for LST validation, and further details about this method are presented in the Methodology Section.

In this study, Landsat 8 data, both daytime and nighttime, were considered for LST retrieval from RTE, SCA, and MWA methods. In the study, SWA was not examined since the USGS do not recommend using Band 11 of Landsat 8 for LST retrieval due to the large calibration uncertainty. Furthermore, we already obtained better results with MWA than with the SWA developed by Mao et al. [36] with coefficients by Yu et al. [51] in our previous research [34]. Considering the literature, in general, researchers have used daytime Landsat data to retrieve LST due to the lack of LSE images in the night. To the best of our knowledge, there is no study published so far that considered nighttime TIR data of Landsat 8 for both retrieval and validation of nighttime LST. Even though the availability of the nighttime Landsat TIR data is limited in time and many researchers are not even aware that Landsat missions acquire nighttime TIR data, it is probable that future Landsat missions may provide much more nighttime TIR data for the sustainability and strength of the scientific studies. As discussed in the previous paper of the authors [34], Normalized Difference Vegetation Index (NDVI)-based LSE retrieval methods are operative and easy to apply for the Landsat data. In this paper, we propose using daytime NDVI-based LSE, whose acquisition is close to nighttime data (the difference ranges from 1 day to 4 days), as an input in the corresponding methods for the nighttime LST retrieval. Besides, the effect of six different NDVI-based LSE models on LST retrieval methods was evaluated for both daytime and nighttime LST analyses. As stated in the day–night algorithm [67], the LSE does not vary dramatically in several days if snow and/or rain does not exist during a short period. Thus, we assumed that the daytime LSE will not change in the night for a few days considering the weather condition of the corresponding time interval. The objectives of this study are to (1) evaluate the efficiency of RTE, MWA, and SCA methods for both daytime and nighttime Landsat 8 data and in-situ measurements, (2) reveal the impact of NDVI-based LSE models on LST retrieval methods for both daytime and nighttime data, (3) encourage the researchers by showing the convenience of the proposed nighttime LST retrieval from Landsat 8 data for the common usage, and (4) provide sensitivity analyses of in-situ measurements and LST retrieval methods for both daytime and nighttime data. Concerning the ground-based LST measurements, upwelling and downwelling thermal radiation measurements were obtained from Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) stations, established over rural areas, simultaneously with TIR data acquisitions. To carry out the image-processing tasks, we used an automated LST retrieval toolbox, which was provided by the authors for the use of researchers in the previous study [34].

#### **2. Datasets**

#### *2.1. In-Situ LST Measurements and Validation Sites*

Surface longwave radiation measurements are important sources for the estimation of in-situ LST and emissivity [65,68]. There are some programs, namely, SURFRAD [69], FLUXNET [70], ARM [71], and Baseline Surface Radiation Network (BSRN) [72] that provide long-term and high-quality surface longwave radiation measurements open to the public. In this study, four stations from SURFRAD and five stations from ARM, nine stations in total (Figure 1) over rural areas, were utilized to calculate daytime and nighttime in-situ LST simultaneous with TIR data acquisitions.

**Figure 1.** Illustration of the locations and surface covers of the Surface Radiation Budget Network (SURFRAD) and Atmospheric Radiation Measurement (ARM) stations used in this study.

The SURFRAD network was established by National Oceanic and Atmospheric Administration (NOAA) in 1993 to support climate-related research over the United States (US) by providing long-term, continuous, and accurate in-situ surface radiation budget [69]. In 1995, the system started operating with four stations, and now, seven SURFRAD stations have been serving in different climatological regions of the US. The SURFRAD data have been utilized in different studies involving assessment of satellite-based retrievals of surface radiation parameters, climate models, hydrology, and validation of radiation transfer codes and surface physics packages of weather [69]. To calculate in-situ LST, quality-controlled measurements of broadband hemispherical upwelling and downwelling longwave radiation are provided by the SURFRAD stations every 3 min (before 2009) or every minute (after 2009). Many studies have been carried out using SURFRAD measurements to validate LST retrievals from satellites [34,47,54,73–75].

The ARM Program was initially founded in 1989 by the US Department of Energy to examine cloud formation processes. Then, the ARM Climate Research Facility was established in 2003, and this program added further sites and instruments to the available ones as a scientific user facility. All data, providing long-term continuous atmospheric measurements, have been freely available since 2003 (https://www.arm.gov/) [76]. Eastern North Atlantic (ENA), North Slope of Alaska (NSA), and Southern Great Plains (SGP) are three basic ARM sites. In this study, five SGP sites were used for in-situ LST retrieval. As in SURFRAD stations, ARM SGP stations provide quality-controlled measurements of upwelling and downwelling longwave radiation for in-situ LST calculation, and many types of

research were carried out using these stations [61,77–79]. Table 1 presents detailed information about both ARM SGP sites and SURFRAD sites considered in this study.


**Table 1.** Characteristics of the SURFRAD and ARM Southern Great Plains (SGP) validation sites used in the study.

In the validation sites, two pyrgeometers (Eppley Precision Infrared Radiometer) mounted at 10-m height measure the downwelling and upwelling longwave radiation in the spectral range from 4.0 to 50.0 µm. The instruments are exchanged annually with newly calibrated instruments at each station [69] and world-recognized organizations perform these calibrations [65]. The Eppley pyrgeometer has about 4.2 W·m−<sup>2</sup> measurement accuracy, and the instrument's precision is around 2 W·m−<sup>2</sup> for daytime measurements and less than 1 W·m−<sup>2</sup> for nighttime measurements [80]. Furthermore, Guillevic et al.[75] reported that considering the instrumental error, less than 1 K uncertainty is observed from the retrieved LST. In this study, we also conducted sensitivity/uncertainty analyses for both daytime and nighttime in-situ measurements in Section 4.1. The spatial representativeness of the pyrgeometer is about 70 m × 70 m at the surface [34,65], which is appropriate for the Landsat TIR pixel size (100 m native resampled at 30 m by the US Geological Survey) over homogeneous surfaces. Thus, we selected the validation sites whose footprint on Landsat 8 TIR pixel has homogeneous surface cover. On the other hand, many studies have already considered these ARM SGP and SURFRAD stations to validate low-resolution LST products of MODIS, SEVIRI, GOES, VIIRS, and AATSR [46,58,65,81,82]. Therefore, the use of these stations in the validation of Landsat-derived LST products is highly acceptable.

### *2.2. Satellite Data*

The Landsat mission has been providing moderate-resolution earth observation data from space regularly for almost 50 years. Landsat 8 was launched on 11 February 2013, and it is the recent operational satellite of the Landsat series. Landsat 4 was the first mission providing one thermal band, and the first TIR data of Landsat 4 dates back to 1982, which makes it possible to study long-term LST variations together with all Landsat missions both at a regional and local scale. The Landsat 8 satellite carries two sensors, namely, the Operational Land Imager (OLI) and the TIR sensor (TIRS). The TIRS sensor has two thermal bands (Band 10 and Band 11), while the OLI sensor has nine reflective bands with 30-m spatial resolution. The native spatial resolution of TIR bands is 100-m; however, USGS publishes them at 30-m by resampling.

In this study, 21 pairs of nighttime and daytime Landsat-8 data (Collection 1) from 2013 to 2019 were utilized for the retrieval of daytime and nighttime LST images. Landsat 8 data were freely obtained through the website of the USGS (https://earthexplorer.usgs.gov/). Band 10 of the TIRS sensor, and Band 4 (Red (R)) and Band 5 (Near Infrared (NIR)) of the OLI sensor, for the estimation of NDVI-based LSE, were used in LST retrieval methods. The quality of the used data was checked by the Pixel Quality Assessment (QA) band that provides information for the exclusion of observations affected by sensor factors, clouds, and cloud shadow [83]. The list of the daytime and nighttime Landsat 8 images with corresponding validation site names are reported in Appendix A.

#### **3. Methodologies**
