# Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Data Set

#### 2.1. Landsat Imagery

#### 2.2. SURFRAD Data and Validation Sites

## 3. LST Retrieval Methods

#### 3.1. Mono Window Algorithm

_{s}is the LST in Kelvin, T is the at-sensor brightness temperature in Kelvin, T

_{a}is the effective mean atmospheric temperature in Kelvin, τ is the atmospheric transmittance, ε represents LSE, a and b are the algorithm constants, C and D are the algorithm parameters calculated using LSE and transmittance. A detailed description of the computations of the T, T

_{a}and τ parameters adopted in this work, are reported in Appendix B. The different LSE models tested in this work will be described in Section 4.

#### 3.2. Single-Channel Algorithm

_{s}) can be computed using the following general equation:

_{sen}is the at-sensor radiance of thermal band, ψ

_{1}, ψ

_{2}, and ψ

_{3}are atmospheric functions, and γ, δ are two parameters given by:

_{γ}= c

_{2}/λ

_{i}with c

_{2}= 14,387.7 µm∙K and λ

_{i}is the effective band wavelength for band i, which is defined as:

_{i}(λ) is the spectral response function for the corresponding band. λ

_{1, i}and λ

_{2, i}are the lower and upper boundary of f

_{i}(λ), respectively. The value of b

_{γ}is equal to 1256 K and 1277 K for Band 6 of Landsat 5 and Landsat 7, respectively; for Band 10 and Band 11 of Landsat 8, it is equal to 1320 K and 1199 K, respectively.

_{1}, ψ

_{2}, and ψ

_{3}are defined as:

^{−2}∙sr

^{−1}∙μm

^{−1}) is upwelling or atmospheric path radiance, ${\mathrm{L}}_{\mathsf{\lambda}}^{\downarrow}$ (W∙m

^{−2}∙sr

^{−1}∙μm

^{−1}) is downwelling or sky radiance. In this study, the atmospheric parameters τ, ${\mathrm{L}}_{\mathsf{\lambda}}^{\uparrow}$ and ${\mathrm{L}}_{\mathsf{\lambda}}^{\downarrow}$ used for the ψ

_{1}, ψ

_{2}, and ψ

_{3}computation are reported in Appendix B.

#### 3.3. Radiative Transfer Equation Method

^{−2}∙sr

^{−1}∙μm

^{−1}) is at-sensor registered radiance of the related thermal band, B

_{λ}(W∙m

^{−2}∙sr

^{−1}∙μm

^{−1}) is the blackbody radiance. Blackbody radiance (B

_{λ}) at a temperature of T

_{s}can be obtained by inverting the Equation (7):

_{s}can be obtained by inverting Planck’s law as:

_{1}and K

_{2}are calibration constants for Landsat data reported in Appendix B.

#### 3.4. Split-Window Algorithm

_{s}) can be calculated using the following equations:

_{10}and L

_{11}can be computed from Table 2 within a specific brightness temperature range for Band 10 (T

_{10}) and Band 11 (T

_{11}), respectively. In Table 2, “a” is the slope and B(K) is the intercept of linear regression. For example, if the brightness temperature of B

_{10}ranges between 20 and 50 °C, L

_{10}can be calculated by 0.4464 * T

_{10}− 66.61.

## 4. Land Surface Emissivity (LSE) Models

#### 4.1. LSE Model of Van de Griend and Owe

#### 4.2. LSE Model of Valor and Caselles

_{max}= 0.5 and NDVI

_{min}= 0.2 in a global situation [70]. As Valor and Caselles [82] suggested, ${\mathsf{\epsilon}}_{\mathrm{v}}$ and ${\mathsf{\epsilon}}_{\mathrm{s}}$ as 0.985 and 0.960, respectively, for unknown emissivity and vegetation structures, we also regarded these emissivity values in the calculation. Besides, they calculated the mean value for $\langle \mathrm{d}\mathsf{\epsilon}\rangle $ term as 0.015, and we utilized this value in LSE retrieval with this model. The final version of the LSE model can be given by:

#### 4.3. NDVI Threshold (NDVI^{THM})-Based LSE Models

^{THM}) values considering three different cases as presented in Equation (21). In the first case (NDVI < 0.2), the pixel is considered as bare soil, and the emissivity is obtained from the reflectance values in the red region. In the second case (0.2 ≤ NDVI ≤ 0.5), the pixel is composed of a mixture of bare soil and vegetation, and in the third case (NDVI > 0.5), the pixels with NDVI values higher than 0.5 are considered as fully vegetated areas.

^{THM}for all models mentioned above.

## 5. LST Computation Using Ground-Based SURFRAD Data

^{2}, respectively, measured during satellite passages. $\mathsf{\sigma}$ is the Stefan–Boltzmann constant (5.670367 × 10

^{−8}W∙m

^{−2}∙K

^{−4}), and ${\mathsf{\epsilon}}_{\mathrm{b}}$ represents the broadband longwave surface emissivity, which is not measured by the station instruments. In previous studies on SURFRAD stations [53,56,65], the broadband emissivity was computed as reported in [104,105] by regression from narrowband emissivity of MODIS thermal bands, which are available through the MODIS monthly emissivity data set. The results in [104,105] proved that the longwave broadband emissivity for the SURFRAD sites could be considered 0.97, as also assumed in [64] and [53].

## 6. Results

#### 6.1. Results of LST Algorithms and LSE Models Derived from Landsat 5 TM

#### 6.2. Results of LST Algorithms and LSE Models Derived from Landsat 7 ETM+

#### 6.3. Results of LST Algorithms and LSE Models Derived from Landsat 8 OLI/TIRS Data

#### 6.4. Comparison of LST Retrieval Algorithms Considering All Landsat Missions

_{a}instead of ${\mathrm{L}}_{\mathsf{\lambda}}^{\uparrow}$ and ${\mathrm{L}}_{\mathsf{\lambda}}^{\downarrow}$. We must also consider the different formulation of the methods: since SCA is derived from a mathematical approximation of RTE [41], it is expected they provide similar results.

#### 6.5. Analysis of Spatio-Temporal and Seasonal LST Variations Between LST Retrieval Methods

^{2}rural area, acquired on 27 April 2018 and covering the BND station, for the three methods used in this analysis.

^{2}selected areas, the minimum LST values from satellite data for spring, summer, and autumn are 280.29 K, 281.70 K, and 284.67 K, respectively; the maximum are 321.88 K, 330.67 K, and 317.95 K, respectively. Figure 3 shows the box-plot graph presenting the seasonal RMS differences between the LST retrieval methods. The box-plot is used to display distributional characteristics of data [106]. The box-plot information, reported in Figure 3 by numbers, is the minimum (1) (the lowest data point excluding any outliers), first quartile (2), median (3), third quartile (4) and maximum (5) (the largest data point excluding any outliers). The cross “x” in the boxes refers to the mean value of the data set, and the points outside the minimum, and maximum values are assumed as outliers. Concerning Figure 3, blue box-plots represent the RMS differences between MWA and RTE-based LST values across the seasons. Red and orange box-plots refer to the RMS differences between RTE and SCA-based LST values, and SCA and MWA-based LST values, respectively.

_{a}instead of ${\mathrm{L}}_{\mathsf{\lambda}}^{\uparrow}$ and ${\mathrm{L}}_{\mathsf{\lambda}}^{\downarrow}$ considered by RTE and SCA. Although the median values of all box-plots and seasons are close to zero (0.11–0.35 K for spring, 0.18–0.94 K for summer, and 0.12–0.43 K for autumn), MWA provides clearly different LST values than RTE and SCA in some summer images. In addition, the mean RMS differences (the cross “x” in boxes) (0.11–0.59 K for spring, 0.24–1.91 K for summer, and 0.12–0.64 K for autumn) reveals the higher variations between MWA and the other two methods in summer. Besides, there are two evident outliers over the maximum value in the summer and autumn for MWA-RTE and SCA-MWA.

#### 6.6. Automated LST Extraction Toolbox for Landsat Missions

## 7. Discussion

## 8. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Table A1.**Information about Landsat data used in the study (45 images from 2000 to 2019). The last column shows the SURFRAD station included within the satellite scene. SURFRAD codes can be found in: https://www.esrl.noaa.gov/gmd/grad/surfrad/sitepage.html or in Table A1.

Sensor | Scene ID | Scene Acquisition Date and Time (UTC) | Path-Row | T_{o} (°C) | RH (%) | NDVI Value | SURFRAD Station |
---|---|---|---|---|---|---|---|

LANDSAT 5 TM | LT50230322007167PAC01 | 16/06/2007–16:29 | 23–32 | 30.8 | 35.3 | 0.348 | BND |

LT50220322008243GNC01 | 30/08/2008–16:15 | 22–32 | 25.5 | 51.2 | 0.672 | ||

LT50230322010255PAC01 | 12/09/2010–16:26 | 23–32 | 24.8 | 32.2 | 0.438 | ||

LT50400352006267PAC01 | 24/09/2006–18:15 | 40–35 | 21.8 | 14.5 | 0.074 | DRA | |

LT50400352007142PAC01 | 22/05/2007–18:16 | 40–35 | 20.7 | 9.6 | 0.075 | ||

LT50400352011281PAC01 | 08/10/2011–18:10 | 40–35 | 16.8 | 31.3 | 0.088 | ||

LT50350262006136PAC01 | 16/05/2006–17:39 | 35–26 | 23.8 | 26.5 | 0.228 | FPK | |

LT50350262008238PAC01 | 25/08/2008–17:32 | 35–26 | 30.2 | 35.1 | 0.170 | ||

LT50360262011253PAC01 | 10/09/2011–17:43 | 36–26 | 27.6 | 28.6 | 0.330 | ||

LT50230362002249LGS01 | 06/09/2002–16:11 | 23–36 | 29.4 | 55.7 | 0.566 | GWN | |

LT50230362008218PAC01 | 05/08/2008–16:23 | 23–36 | 30.6 | 60.2 | 0.578 | ||

LT50230362011242PAC01 | 30/08/2011–16:26 | 23–36 | 31.2 | 33.6 | 0.552 | ||

LT50160322003267GNC02 | 24/09/2003–15:30 | 16–32 | 15.9 | 57.5 | 0.698 | PSU | |

LT50160322008233GNC01 | 20/08/2008–15:39 | 16–32 | 19.5 | 50.2 | 0.674 | ||

LT50160322009139GNC01 | 19/05/2009–15:40 | 16–32 | 15.9 | 29.9 | 0.390 | ||

LANDSAT 7 ETM+ | LE70230322000284EDC00 | 10/10/2000–16:26 | 23–32 | 13.0 | 35.5 | 0.515 | BND |

LE70230322001254EDC00 | 11/09/2001–16:24 | 23–32 | 24.1 | 40.0 | 0.535 | ||

LE70220322002186EDC00 | 05/07/2002–16:18 | 22–32 | 30.7 | 51.4 | 0.097 | ||

LE70400352001165EDC00 | 14/06/2001–18:12 | 40–35 | 23.3 | 12.8 | -0.025 | DRA | |

LE70400352001213EDC00 | 01/08/2001–18:11 | 40–35 | 32.5 | 10.9 | -0.030 | ||

LE70400352002168EDC00 | 17/06/2002–18:10 | 40–35 | 30.9 | 6.9 | -0.027 | ||

LE70350262000112EDC00 | 21/04/2000–17:39 | 35–26 | 21.2 | 22.6 | -0.068 | FPK | |

LE70360262001217EDC00 | 05/08/2001–17:43 | 36–26 | 28.2 | 32.8 | -0.068 | ||

LE70350262002181EDC00 | 30/06/2002–17:36 | 35–26 | 23.3 | 32.5 | -0.073 | ||

LE70220362000117EDC00 | 26/04/2000–16:23 | 22–36 | 18.9 | 43.4 | 0.300 | GWN | |

LE70230362000220EDC00 | 07/08/2000–16:28 | 23–36 | 32.5 | 55.1 | 0.258 | ||

LE70220362001167EDC00 | 16/06/2001–16:21 | 22–36 | 27.3 | 44.9 | 0.389 | ||

LE70160322000091EDC00 | 31/03/2000–15:45 | 16–32 | 7.8 | 37.7 | -0.029 | PSU | |

LE70160322002192EDC00 | 11/07/2002–15:41 | 16–32 | 17.9 | 44.4 | 0.375 | ||

LE70160322002256EDC00 | 13/09/2002–15:40 | 16–32 | 21.5 | 37.3 | 0.250 | ||

LANDSAT 8 OLI/TIRS | LC80230322013247LGN01 | 04/09/2013–16:38 | 23–32 | 23.9 | 57.2 | 0.621 | BND |

LC80230322018101LGN00 | 11/04/2018–16:35 | 23–32 | 12.8 | 57.2 | 0.421 | ||

LC80230322018117LGN00 | 27/04/2018–16:35 | 23–32 | 15.2 | 32.4 | 0.622 | ||

LC80400352017121LGN00 | 01/05/2017–18:22 | 40–35 | 23.5 | 14.0 | 0.110 | DRA | |

LC80400352018124LGN00 | 04/05/2018–18:21 | 40–35 | 26.4 | 14.6 | 0.108 | ||

LC80400352018236LGN00 | 24/08/2018–18:22 | 40–35 | 32.8 | 8.7 | 0.088 | ||

LC80350262017198LGN00 | 17/07/2017–17:48 | 35–26 | 24.7 | 22.1 | 0.213 | FPK | |

LC80360262018160LGN00 | 09/06/2018–17:53 | 36–26 | 27.5 | 51.2 | 0.370 | ||

LC80350262018249LGN00 | 06/09/2018–17:47 | 35–26 | 21.8 | 38.4 | 0.232 | ||

LC80220362016281LGN01 | 07/10/2016–16:32 | 22–36 | 27.5 | 44.0 | 0.410 | GWN | |

LC80220362017251LGN00 | 08/09/2017–16:32 | 22–36 | 22.5 | 44.8 | 0.626 | ||

LC80220362018094LGN00 | 04/04/2018–16:31 | 22–36 | 8.6 | 43.1 | 0.397 | ||

LC80160322015124LGN01 | 04/05/2015–15:51 | 16–32 | 24.3 | 24.1 | 0.365 | PSU | |

LC80160322016111LGN01 | 20/04/2016–15:51 | 16–32 | 15.2 | 15.2 | 0.512 | ||

LC80160322019263LGN00 | 20/09/2019–15:53 | 16–32 | 20.6 | 53.8 | 0.637 |

## Appendix B

#### Appendix B.1. Brightness Temperature (T) Retrieval

_{λ}is Top of Atmosphere (TOA) spectral radiance (Watts/(m

^{2}∙srad∙μm)), Q

_{CAL}is the quantized calibrated pixel value in DN, L

_{MINλ}(Watts/(m

^{2}∙srad∙μm)) is the spectral radiance scaled to QCAL

_{MIN}, L

_{MAXλ}(Watts/(m

^{2}∙srad∙μm)) is the spectral radiance scaled to QCAL

_{MAX}, QCAL

_{MIN}is the minimum quantized calibrated pixel value in DN and QCAL

_{MAX}is the maximum quantized calibrated pixel value in DN. L

_{MINλ}, L

_{MAXλ}, QCAL

_{MIN}, and QCAL

_{MAX}values are obtained from the metadata file of Landsat TM and ETM+ data. For Landsat 8:

^{2}∙srad∙μm)), ${\mathrm{M}}_{\mathrm{L}}$ is the band-specific multiplicative rescaling factor from the metadata, ${\mathrm{A}}_{\mathrm{L}}$ is the band-specific additive rescaling factor from the metadata, ${\mathrm{Q}}_{\mathrm{CAL}}$ is the quantized and calibrated standard product pixel values (DN). All of these variables can be retrieved from the metadata file of Landsat 8 data. After radiance conversion, brightness temperature image can be generated by Equation (A3) for all Landsat missions [126,127].

_{1}(Watts/(m

^{2}∙srad∙μm)) and K

_{2}(Kelvin) are the calibration constants and L

_{λ}is the spectral radiance. The values of the constants (K

_{1}and K

_{2}) were presented in Table A2 since they change from sensor to sensor [126,127].

SATELLITE | K_{1} (Watts/(m^{2}∙srad∙μm)) | K_{2} (Kelvin) |
---|---|---|

Landsat 5 (Band6) | 607.76 | 1260.56 |

Landsat 7 (Band6) | 666.09 | 1282.71 |

Landsat 8 (Band10) | 774.89 | 1321.08 |

Landsat 8 (Band11) | 480.89 | 1201.14 |

#### Appendix B.2. Effective Mean Atmospheric Temperature (T_{a}) Retrieval

_{a}) by means of near-surface temperature (T

_{o}),essential for MWA [43]. In this work, mid-latitude summer region was considered for the calculation.

_{a}in this work; however, the USA 1976 Standard atmosphere is also suitable for our test sites. Thus, we also investigated the difference in LST when using mid-latitude summer and USA 1976 Standard models with simulations, and we obtained almost 1 K difference in LST in the analyses.

**Table A3.**The effective mean atmospheric temperature estimation (T

_{a}) using near-surface air temperature (T

_{o}).

Model | Mean Atmospheric Temperature (T_{a}) in Kelvin |
---|---|

USA 1976 Standard | T_{a} = 25.940 + 0.8805 × T_{o} |

Tropical Region | T_{a} = 17.977 + 0.9172 × T_{o} |

Mid-latitude Summer Region | T_{a} = 16.011 + 0.9262 × T_{o} |

Mid-latitude Winter Region | T_{a} = 19.270 + 0.9112 × T_{o} |

#### Appendix B.3. Atmospheric Transmittance (τ), Upwelling Radiance (${L}_{\lambda}^{\uparrow}$), and Downwelling Radiance (${L}_{\lambda}^{\downarrow}$) Retrieval

_{10}and τ

_{11}) were calculated using water vapor as presented in Table A4 [46].

**Table A4.**The relationship between atmospheric transmittance (τ

_{10/11}) and water vapor content (w).

Model | Water Vapor Range | Equation |
---|---|---|

Mid-latitude Summer Region | 0.2–3.0 g/cm^{2} | ${\mathsf{\tau}}_{10}=-0.0164{\mathrm{w}}^{2}-0.04203\mathrm{w}+0.9715$ |

${\mathsf{\tau}}_{11}=-0.01218{\mathrm{w}}^{2}-0.07735\mathrm{w}+0.9603$ |

_{o}) using the following equation [130]:

_{i}(g/cm

^{2}) is the water vapor content, To is the near-surface temperature in Kelvin, and RH (%) refers to the relative humidity.

## Appendix C

_{λ}is the TOA spectral radiance (Watts/(m

^{2}∙srad∙μm)), d is Earth-Sun distance in astronomical units, ESUN

_{λ}is the mean solar exo-atmospheric spectral irradiances (Watts/(m

^{2}∙μm)) and θ

_{s}is the solar zenith angle in degrees. ESUN

_{λ}values for each band of Landsat 5 and 7 can be obtained from the handbooks of the related mission [126]. θ

_{s}and d values can be attained from the metadata file.

## Appendix D

^{−2}∙sr

^{−1}∙μm

^{−1}and 2.82 W∙m

^{−2}∙sr

^{−1}∙μm

^{−1}. We assumed the brightness temperature range from 285 K to 300 K, since the variation in the brightness temperature also affect the results. The LSE value was fixed as 0.97. Considering SWA, we observed the average difference between ${\mathsf{\tau}}_{10}$ and ${\mathsf{\tau}}_{11}$ as 0.05. Thus, we assumed ${\mathsf{\tau}}_{10}$ and ${\mathsf{\tau}}_{11}$ to be 0.82 and 0.77, respectively. A fixed value of 1.5 K for T

_{10}–T

_{11}as in [131].

Input Parameter | Uncertainty | T_{b} (K) | Estimated impact on LST | |||
---|---|---|---|---|---|---|

MWA | RTE | SCA | SWA | |||

LSE | ±0.01 | 285 | ±0.49 K | ±0.58 K | ±0.54 K | ±0.55 K |

290 | ±0.54 K | ±0.58 K | ±0.56 K | ±0.55 K | ||

295 | ±0.58 K | ±0.58 K | ±0.58 K | ±0.55 K | ||

300 | ±0.63 K | ±0.58 K | ±0.60 K | ±0.55 K | ||

Atmospheric Transmittance | ±0.01 | 285 | ±0.09 K | ±0.97 K | ±0.89 K | ±0.29 K |

290 | ±0.01 K | ±0.97 K | ±0.93 K | ±0.29 K | ||

295 | ±0.08 K | ±0.97 K | ±0.96 K | ±0.29 K | ||

300 | ±0.16 K | ±0.97 K | ±0.99 K | ±0.29 K | ||

Effective Mean Atmospheric Temperature | ±1 K | 285 | ±0.32 K | Not Applicable | Not Applicable | Not Applicable |

290 | ±0.32 K | Not Applicable | Not Applicable | Not Applicable | ||

295 | ±0.32 K | Not Applicable | Not Applicable | Not Applicable | ||

300 | ±0.32 K | Not Applicable | Not Applicable | Not Applicable | ||

${\mathrm{L}}_{\mathsf{\lambda}}^{\uparrow}$ | ±10% | 285 | Not Applicable | ±1.82 K | ±1.66 K | Not Applicable |

290 | Not Applicable | ±1.82 K | ±1.72 K | Not Applicable | ||

295 | Not Applicable | ±1.82 K | ±1.78 K | Not Applicable | ||

300 | Not Applicable | ±1.82 K | ±1.84 K | Not Applicable | ||

${\mathrm{L}}_{\mathsf{\lambda}}^{\downarrow}$ | ±10% | 285 | Not Applicable | ±0.07 K | ±0.06 K | Not Applicable |

290 | Not Applicable | ±0.07 K | ±0.06 K | Not Applicable | ||

295 | Not Applicable | ±0.07 K | ±0.07 K | Not Applicable | ||

300 | Not Applicable | ±0.07 K | ±0.07 K | Not Applicable |

## Appendix E

_{a}); model parameters include Earth-sun distance (d), solar zenith angle (θ

_{sz}) for Landsat 5 and 7, and sun elevation angle (θ

_{se}) for Landsat 8. ${\mathrm{L}}_{\mathsf{\lambda}}^{\uparrow}$, ${\mathrm{L}}_{\mathsf{\lambda}}^{\downarrow}$ and τ were calculated using NASA’s ACPC (see Appendix B.3), and T

_{a}was obtained from Table A3 for mid-latitude summer region. d and θ

_{se}are obtained from metadata file of the Landsat data, and θ

_{sz}is equal to 90

^{o}− θ

_{se}. Earth-sun distance “d” is not necessary for Landsat 8 data to obtain spectral reflectance. Thus, this column in the table is empty for Landsat 8. In addition to Table A6, Table A7 presents transmittance values for Band 10 and Band 11 of Landsat 8 TIRS required for LST retrieval using SWA, and they were calculated from Table A4.

Sensor | Scene Acquisition Date and Time (UTC) | W/(m^{2}*sr*µm) | τ | T_{a} (K) | θ_{sz} (L5-7)/θ_{se} (L8) (°) | d (Astronomical Unit) | |
---|---|---|---|---|---|---|---|

${\mathbf{L}}_{\mathsf{\lambda}}^{\mathbf{\uparrow}}$ | ${\mathbf{L}}_{\mathsf{\lambda}}^{\mathbf{\downarrow}}$ | ||||||

LANDSAT 5 TM | 16/06/2007–16:29 | 2.28 | 3.68 | 0.71 | 297.53 | 24.89 | 1.0159 |

30/08/2008–16:15 | 2.07 | 3.29 | 0.75 | 292.62 | 38.04 | 1.0095 | |

12/09/2010–16:26 | 1.74 | 2.82 | 0.77 | 291.97 | 41.14 | 1.0064 | |

24/09/2006–18:15 | 0.63 | 1.09 | 0.91 | 289.19 | 41.95 | 1.0031 | |

22/05/2007–18:16 | 0.38 | 0.69 | 0.94 | 288.17 | 24.51 | 1.0123 | |

08/10/2011–18:10 | 0.57 | 0.97 | 0.91 | 284.56 | 46.31 | 0.9991 | |

16/05/2006–17:39 | 0.88 | 1.51 | 0.87 | 291.05 | 33.20 | 1.0112 | |

25/08/2008–17:32 | 2.02 | 3.28 | 0.77 | 296.97 | 42.45 | 1.0106 | |

10/09/2011–17:43 | 1.15 | 1.92 | 0.86 | 294.57 | 47.05 | 1.0070 | |

06/09/2002–16:11 | 4.38 | 6.41 | 0.48 | 296.23 | 37.92 | 1.0079 | |

05/08/2008–16:23 | 3.91 | 5.87 | 0.53 | 297.34 | 29.47 | 1.0143 | |

30/08/2011–16:26 | 3.17 | 4.89 | 0.61 | 297.90 | 33.94 | 1.0097 | |

24/09/2003–15:30 | 1.29 | 2.11 | 0.82 | 283.73 | 46.09 | 1.0032 | |

20/08/2008–15:39 | 1.75 | 2.81 | 0.76 | 287.06 | 35.38 | 1.0117 | |

19/05/2009–15:40 | 0.59 | 1.02 | 0.91 | 283.73 | 27.80 | 1.0118 | |

LANDSAT 7 ETM+ | 10/10/2000–16:26 | 0.48 | 0.81 | 0.93 | 281.04 | 50.45 | 0.9984 |

11/09/2001–16:24 | 1.73 | 2.8 | 0.78 | 291.32 | 41.07 | 1.0066 | |

05/07/2002–16:18 | 3.31 | 5.13 | 0.6 | 297.44 | 26.84 | 1.0167 | |

14/06/2001–18:12 | 0.51 | 0.91 | 0.93 | 290.58 | 24.10 | 1.0157 | |

01/08/2001–18:11 | 0.95 | 1.63 | 0.88 | 299.10 | 28.81 | 1.0149 | |

17/06/2002–18:10 | 0.69 | 1.22 | 0.92 | 297.62 | 24.35 | 1.0160 | |

21/04/2000–17:39 | 0.77 | 1.32 | 0.88 | 288.64 | 40.00 | 1.0052 | |

05/08/2001–17:43 | 1.6 | 2.64 | 0.8 | 295.12 | 36.62 | 1.0143 | |

30/06/2002–17:36 | 0.82 | 1.41 | 0.89 | 290.58 | 30.75 | 1.0167 | |

26/04/2000–16:23 | 1.28 | 2.1 | 0.82 | 286.51 | 29.20 | 1.0065 | |

07/08/2000–16:28 | 4.87 | 7.09 | 0.41 | 299.10 | 28.93 | 1.0140 | |

16/06/2001–16:21 | 1.81 | 3.16 | 0.76 | 294.29 | 23.80 | 1.0159 | |

31/03/2000–15:45 | 0.42 | 0.71 | 0.93 | 276.23 | 41.37 | 0.9992 | |

11/07/2002–15:41 | 0.8 | 1.35 | 0.89 | 285.58 | 27.49 | 1.0166 | |

13/09/2002–15:40 | 1.31 | 2.16 | 0.83 | 288.92 | 41.68 | 1.0061 | |

LANDSAT 8 OLI/TIRS | 04/09/2013–16:38 | 1.88 | 3.06 | 0.77 | 291.14 | 52.48 | - |

11/04/2018–16:35 | 0.88 | 1.49 | 0.87 | 280.86 | 53.35 | - | |

27/04/2018–16:35 | 0.49 | 0.85 | 0.93 | 283.08 | 58.58 | - | |

01/05/2017–18:22 | 0.5 | 0.9 | 0.93 | 290.77 | 62.45 | - | |

04/05/2018–18:21 | 0.55 | 0.99 | 0.93 | 293.45 | 63.08 | - | |

24/08/2018–18:22 | 0.64 | 1.15 | 0.93 | 299.38 | 58.18 | - | |

17/07/2017–17:48 | 0.79 | 1.38 | 0.89 | 291.88 | 58.33 | - | |

09/06/2018–17:53 | 2.22 | 3.61 | 0.73 | 294.47 | 60.62 | - | |

06/09/2018–17:47 | 1.43 | 2.38 | 0.81 | 289.19 | 45.00 | - | |

07/10/2016–16:32 | 2.17 | 3.51 | 0.74 | 294.47 | 46.10 | - | |

08/09/2017–16:32 | 1.52 | 2.51 | 0.81 | 289.84 | 55.18 | - | |

04/04/2018–16:31 | 0.35 | 0.6 | 0.94 | 276.97 | 54.73 | - | |

04/05/2015–15:51 | 1.67 | 2.76 | 0.78 | 291.51 | 60.42 | - | |

20/04/2016–15:51 | 0.45 | 0.77 | 0.94 | 283.08 | 56.60 | - | |

20/09/2019–15:53 | 1.12 | 1.88 | 0.86 | 288.08 | 47.37 | - |

**Table A7.**Atmospheric transmittance values for Band 10 and Band 11 of Landsat 8 TIRS data used in SWA.

Sensor | Scene Acquisition Date and Time (UTC) | ${\mathsf{\tau}}_{10}$ | ${\mathsf{\tau}}_{11}$ |
---|---|---|---|

LANDSAT 8 OLI/TIRS | 04/09/2013–16:38 | 0.839 | 0.777 |

11/04/2018–16:35 | 0.913 | 0.871 | |

27/04/2018–16:35 | 0.933 | 0.898 | |

01/05/2017–18:22 | 0.942 | 0.912 | |

04/05/2018–18:21 | 0.936 | 0.904 | |

24/08/2018–18:22 | 0.941 | 0.910 | |

17/07/2017–17:48 | 0.924 | 0.886 | |

09/06/2018–17:53 | 0.820 | 0.755 | |

06/09/2018–17:47 | 0.901 | 0.855 | |

07/10/2016–16:32 | 0.847 | 0.787 | |

08/09/2017–16:32 | 0.883 | 0.832 | |

04/04/2018–16:31 | 0.938 | 0.906 | |

04/05/2015–15:51 | 0.921 | 0.882 | |

20/04/2016–15:51 | 0.951 | 0.925 | |

20/09/2019–15:53 | 0.876 | 0.822 |

## Appendix F

**Figure A2.**The interface of the MWA method using the Landsat 5 TM data and Sobrino et al.’s LSE model.

## References

- Prakash, A. Thermal remote sensing: Concepts, issues and applications. In Proceedings of the International Archives of Photogrammetry and Remote Sensing, Amsterdam, The Netherlands, 16–22 July 2002; Volume 23, pp. 239–243. [Google Scholar]
- Kahle, A.B. Surface thermal properties. In Remote Sensing in Geology; Siegal, B.S., Gillespie, A.R., Eds.; John Wiley & Sons, Inc.: New York, NY, USA, 1980; pp. 257–273. ISBN 0471790524. [Google Scholar]
- Sabins, F.F. Remote Sensing: Principles and Interpretation, 3rd ed.; W. H. Freeman: New York, NY, USA, 1996; ISBN 0716724421. [Google Scholar]
- Wan, Z.; Dozier, J. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote Sens.
**1996**, 34, 892–905. [Google Scholar] - Meng, X.; Cheng, J.; Liang, S. Estimating land surface temperature from Feng Yun-3C/MERSI data using a new land surface emissivity scheme. Remote Sens.
**2017**, 9, 1247. [Google Scholar] [CrossRef][Green Version] - Zhou, D.; Xiao, J.; Bonafoni, S.; Berger, C.; Deilami, K.; Zhou, Y.; Frolking, S.; Yao, R.; Qiao, Z.; Sobrino, J. Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. Remote Sens.
**2018**, 11, 48. [Google Scholar] [CrossRef][Green Version] - Li, Z.; Tang, B.-H.; Wu, H.; Ren, H.; Yan, G.; Wan, Z.; Trigo, I.F.; Sobrino, J.A. Satellite-derived land surface temperature: Current status and perspectives. Remote Sens. Environ.
**2013**, 131, 14–37. [Google Scholar] [CrossRef][Green Version] - Kerr, Y.H.; Lagouarde, J.P.; Nerry, F.; Ottlé, C. Land surface temperature retrieval techniques and applications. In Thermal Remote Sensing in Land Surface Processing; Quattrochi, D.A., Luvall, J.C., Eds.; CRC Press: Boca Raton, FL, USA, 2000; pp. 33–109. [Google Scholar]
- Karnieli, A.; Agam, N.; Pinker, R.T.; Anderson, M.; Imhoff, M.L.; Gutman, G.G.; Panov, N.; Goldberg, A. Use of NDVI and land surface temperature for drought assessment: Merits and limitations. J. Clim.
**2010**, 23, 618–633. [Google Scholar] [CrossRef] - Brunsell, N.A.; Gillies, R.R. Length scale analysis of surface energy fluxes derived from remote sensing. J. Hydrometeorol.
**2003**, 4, 1212–1219. [Google Scholar] [CrossRef] - Kustas, W.; Anderson, M. Advances in thermal infrared remote sensing for land surface modeling. Agric. For. Meteorol.
**2009**, 149, 2071–2081. [Google Scholar] [CrossRef] - Dickinson, R.E. Land surface processes and climate—Surface albedos and energy balance. Adv. Geophys.
**1983**, 25, 305–353. [Google Scholar] - Fang, L.; Zhan, X.; Hain, C.; Yin, J.; Liu, J.; Schull, M. An assessment of the impact of land thermal infrared observation on regional weather forecasts using two different data assimilation approaches. Remote Sens.
**2018**, 10, 625. [Google Scholar] [CrossRef][Green Version] - Dash, P.; Göttsche, F.-M.; Olesen, F.-S.; Fischer, H. Land surface temperature and emissivity estimation from passive sensor data: Theory and practice—Current trends. Int. J. Remote Sens.
**2002**, 23, 2563–2594. [Google Scholar] [CrossRef] - Martin, M.; Ghent, D.; Pires, A.; Göttsche, F.-M.; Cermak, J.; Remedios, J. Comprehensive in situ validation of five satellite land surface temperature data sets over multiple stations and years. Remote Sens.
**2019**, 11, 479. [Google Scholar] [CrossRef][Green Version] - Naughton, J.; McDonald, W. Evaluating the variability of urban land surface temperatures using drone observations. Remote Sens.
**2019**, 11, 1722. [Google Scholar] [CrossRef][Green Version] - Bonafoni, S.; Anniballe, R.; Gioli, B.; Toscano, P. Downscaling Landsat land surface temperature over the urban area of Florence. Eur. J. Remote Sens.
**2016**, 49, 553–569. [Google Scholar] [CrossRef] - Sekertekin, A.; Kutoglu, S.H.; Kaya, S. Evaluation of spatio-temporal variability in land surface temperature: A case study of Zonguldak, Turkey. Environ. Monit. Assess.
**2016**, 188, 30. [Google Scholar] [CrossRef] - Simwanda, M.; Ranagalage, M.; Estoque, R.C.; Murayama, Y. Spatial analysis of surface urban heat islands in four rapidly growing African cities. Remote Sens.
**2019**, 11, 1645. [Google Scholar] [CrossRef][Green Version] - Li, F.; Sun, W.; Yang, G.; Weng, Q. Investigating spatiotemporal patterns of surface urban heat islands in the Hangzhou Metropolitan Area, China, 2000–2015. Remote Sens.
**2019**, 11, 1553. [Google Scholar] [CrossRef][Green Version] - Senay, G.B.; Schauer, M.; Velpuri, N.M.; Singh, R.K.; Kagone, S.; Friedrichs, M.; Litvak, M.E.; Douglas-Mankin, K.R. Long-term (1986–2015) crop water use characterization over the upper Rio Grande Basin of United States and Mexico using Landsat-based evapotranspiration. Remote Sens.
**2019**, 11, 1587. [Google Scholar] [CrossRef][Green Version] - Maffei, C.; Alfieri, S.; Menenti, M. Relating spatiotemporal patterns of forest fires burned area and duration to diurnal land surface temperature anomalies. Remote Sens.
**2018**, 10, 1777. [Google Scholar] [CrossRef][Green Version] - Sekertekin, A.; Arslan, N. Monitoring thermal anomaly and radiative heat flux using thermal infrared satellite imagery—A case study at Tuzla geothermal region. Geothermics
**2019**, 78, 243–254. [Google Scholar] [CrossRef] - Coolbaugh, M.F.; Kratt, C.; Fallacaro, A.; Calvin, W.M.; Taranik, J.V. Detection of geothermal anomalies using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images at Bradys Hot Springs, Nevada, USA. Remote Sens. Environ.
**2007**, 106, 350–359. [Google Scholar] [CrossRef] - Eskandari, A.; De Rosa, R.; Amini, S. Remote sensing of Damavand volcano (Iran) using Landsat imagery: Implications for the volcano dynamics. J. Volcanol. Geotherm. Res.
**2015**, 306, 41–57. [Google Scholar] [CrossRef] - Mia, M.; Fujimitsu, Y.; Nishijima, J. Monitoring of thermal activity at the Hatchobaru–Otake geothermal area in Japan using multi-source satellite images—With comparisons of methods, and solar and seasonal effects. Remote Sens.
**2018**, 10, 1430. [Google Scholar] [CrossRef][Green Version] - Hulley, G.C.; Hook, S.J. The North American ASTER Land Surface Emissivity Database (NAALSED) version 2.0. Remote Sens. Environ.
**2009**, 113, 1967–1975. [Google Scholar] [CrossRef] - Townshend, J.R.G.R.; Justice, C.O.O.; Skole, D.; Malingreau, J.-P.P.; Cihlar, J.; Teillet, P.; Sadowski, F.; Ruttenberg, S. The 1 km resolution global data set: Needs of the international geosphere biosphere programme! Int. J. Remote Sens.
**1994**, 15, 3417–3441. [Google Scholar] [CrossRef] - Becker, F.; Li, Z.-L. Surface temperature and emissivity at various scales: Definition, measurement and related problems. Remote Sens. Rev.
**1995**, 12, 225–253. [Google Scholar] [CrossRef] - Hale, R.C.; Gallo, K.P.; Tarpley, D.; Yu, Y. Characterization of variability at in situ locations for calibration/validation of satellite-derived land surface temperature data. Remote Sens. Lett.
**2011**, 2, 41–50. [Google Scholar] [CrossRef] - Gao, C.; Jiang, X.; Li, Z.-L.; Nerry, F. Comparison of the thermal sensors of SEVIRI and MODIS for LST mapping. In Thermal Infrared Remote Sensing; Remote Sensing and Digital Image Processing Book Series; Kuenzer, C., Dech, S., Eds.; Springer: Dordrecht, The Netherlands, 2013; Volume 17, pp. 233–252. [Google Scholar]
- Dash, P.; Göttsche, F.-M.; Olesen, F.; Fischer, H. Retrieval of land surface temperature and emissivity from satellite data: Physics, theoretical limitations and current methods. J. Indian Soc. Remote Sens.
**2001**, 29, 23–30. [Google Scholar] [CrossRef] - Li, Z.-L.; Becker, F. Feasibility of land surface temperature and emissivity determination from AVHRR data. Remote Sens. Environ.
**1993**, 43, 67–85. [Google Scholar] [CrossRef] - Schmugge, T.; French, A.; Ritchie, J.C.; Rango, A.; Pelgrum, H. Temperature and emissivity separation from multispectral thermal infrared observations. Remote Sens. Environ.
**2002**, 79, 189–198. [Google Scholar] [CrossRef] - Sobrino, J.; Jimenez Munoz, J.; Verhoef, W. Canopy directional emissivity: Comparison between models. Remote Sens. Environ.
**2005**, 99, 304–314. [Google Scholar] [CrossRef] - Sobrino, J.A.; Raissouni, N.; Li, Z. A comparative study of land surface emissivity retrieval from NOAA data. Remote Sens. Environ.
**2001**, 75, 256–266. [Google Scholar] [CrossRef] - Liu, X.; Tang, B.; Yan, G.; Li, Z.-L.; Liang, S. Retrieval of global orbit drift corrected land surface temperature from long-term AVHRR data. Remote Sens.
**2019**, 11, 2843. [Google Scholar] [CrossRef][Green Version] - Ghent, D.; Veal, K.; Trent, T.; Dodd, E.; Sembhi, H.; Remedios, J. A new approach to defining uncertainties for MODIS land surface temperature. Remote Sens.
**2019**, 11, 1021. [Google Scholar] [CrossRef][Green Version] - Becker, F.; Li, Z.L. Toward a local split window method over land surface. Int. J. Remote Sens.
**1990**, 11, 369–393. [Google Scholar] [CrossRef] - Gillespie, A.; Rokugawa, S.; Matsunaga, T.; Steven Cothern, J.; Hook, S.; Kahle, A.B. A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer (ASTER) images. IEEE Trans. Geosci. Remote Sens.
**1998**, 36, 1113–1126. [Google Scholar] [CrossRef] - Jiménez-Muñoz, J.C.; Sobrino, J.A. A generalized single-channel method for retrieving land surface temperature from remote sensing data. J. Geophys. Res.
**2003**, 109, 8112. [Google Scholar] [CrossRef][Green Version] - Price, J.C. Estimating surface temperatures from satellite thermal infrared data—A simple formulation for the atmospheric effect. Remote Sens. Environ.
**1983**, 13, 353–361. [Google Scholar] [CrossRef] - Qin, Z.; Karnieli, A.; Berliner, P. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int. J. Remote Sens.
**2001**, 22, 3719–3746. [Google Scholar] [CrossRef] - Jiménez-Muñoz, J.C.; Cristóbal, J.; Sobrino, J.A.; Sòria, G.; Ninyerola, M.; Pons, X. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Trans. Geosci. Remote Sens.
**2009**, 47, 339–349. [Google Scholar] [CrossRef] - Mao, K.; Qin, Z.; Shi, J.; Gong, P. A practical split-window algorithm for retrieving land-surface temperature from MODIS data. Int. J. Remote Sens.
**2005**, 26, 3181–3204. [Google Scholar] [CrossRef] - Yu, X.; Guo, X.; Wu, Z. Land surface temperature retrieval from Landsat 8 TIRS-comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sens.
**2014**, 6, 9829–9852. [Google Scholar] [CrossRef][Green Version] - Wan, Z.; Li, Z.-L. Radiance-based validation of the V5 MODIS land-surface temperature product. Int. J. Remote Sens.
**2008**, 29, 5373–5395. [Google Scholar] [CrossRef] - Peres, L.F.; Sobrino, J.A.; Libonati, R.; Jiménez-Muñoz, J.C.; Dacamara, C.C.; Romaguera, M. Validation of a temperature emissivity separation hybrid method from airborne hyperspectral scanner data and ground measurements in the SEN2FLEX field campaign. Int. J. Remote Sens.
**2008**, 29, 7251–7268. [Google Scholar] [CrossRef][Green Version] - Coll, C.; Caselles, V.; Galve, J.; Valor, E.; Niclos, R.; Sanchez, J.; Rivas, R. Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data. Remote Sens. Environ.
**2005**, 97, 288–300. [Google Scholar] [CrossRef] - Wan, Z.; Zhang, Y.; Zhang, Q.; Li, Z.-L. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sens. Environ.
**2002**, 83, 163–180. [Google Scholar] [CrossRef] - Sabol, D.E., Jr.; Gillespie, A.R.; Abbott, E.; Yamada, G. Field validation of the ASTER temperature–emissivity separation algorithm. Remote Sens. Environ.
**2009**, 113, 2328–2344. [Google Scholar] [CrossRef] - Meng, X.; Cheng, J.; Zhao, S.; Liu, S.; Yao, Y. Estimating land surface temperature from Landsat-8 data using the NOAA JPSS enterprise algorithm. Remote Sens.
**2019**, 11, 155. [Google Scholar] [CrossRef][Green Version] - Zhang, Z.; He, G.; Wang, M.; Long, T.; Wang, G.; Zhang, X. Validation of the generalized single-channel algorithm using Landsat 8 imagery and SURFRAD ground measurements. Remote Sens. Lett.
**2016**, 7, 810–816. [Google Scholar] [CrossRef] - Wang, M.; Zhang, Z.; Hu, T.; Liu, X. A practical single-channel algorithm for land surface temperature retrieval: Application to Landsat series data. J. Geophys. Res. Atmos.
**2019**, 124, 299–316. [Google Scholar] [CrossRef] - Malakar, N.K.; Hulley, G.C.; Hook, S.J.; Laraby, K.; Cook, M.; Schott, J.R. An operational land surface temperature product for Landsat thermal data: Methodology and validation. IEEE Trans. Geosci. Remote Sens.
**2018**, 56, 5717–5735. [Google Scholar] [CrossRef] - Wang, S.; He, L.; Hu, W. A temperature and emissivity separation algorithm for Landsat-8 thermal infrared sensor data. Remote Sens.
**2015**, 7, 9904–9927. [Google Scholar] [CrossRef][Green Version] - Zhang, Z.; He, G.; Wang, M.; Long, T.; Wang, G.; Zhang, X.; Jiao, W. Towards an operational method for land surface temperature retrieval from Landsat 8 data. Remote Sens. Lett.
**2016**, 7, 279–288. [Google Scholar] [CrossRef] - Sobrino, J.A.; Jimenez-Muoz, J.C.; Soria, G.; Romaguera, M.; Guanter, L.; Moreno, J.; Plaza, A.; Martinez, P. Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Trans. Geosci. Remote Sens.
**2008**, 46, 316–327. [Google Scholar] [CrossRef] - Sekertekin, A. Validation of physical radiative transfer equation-based land surface temperature using Landsat 8 satellite imagery and SURFRAD in-situ measurements. J. Atmos. Solar Terr. Phys.
**2019**, 196, 105161. [Google Scholar] [CrossRef] - Skokovic, D.; Sobrino, J.A.; Jiménez Muñoz, J.C.; Soria, G.; Julien, Y.; Mattar, C.; Cristóbal, J. Calibration and validation of land surface temperature for Landsat8-TIRS sensor TIRS Landsat-8 characteristics. L. Prod. Valid. Evol. ESA/ESRIN
**2014**, 27. Available online: https://earth.esa.int/documents/700255/2126408/ESA_Lpve_Sobrino_2014a.pdf (accessed on 11 January 2020). - Cook, M.; Schott, J.; Mandel, J.; Raqueno, N. Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a land surface temperature (LST) product from the archive. Remote Sens.
**2014**, 6, 11244–11266. [Google Scholar] [CrossRef][Green Version] - Cook, M. Atmospheric Compensation for a Landsat Land Surface Temperature Product. Ph.D. Thesis, Rochester Institute of Technology, Rochester, NY, USA, 2014. [Google Scholar]
- Augustine, J.A.; DeLuisi, J.J.; Long, C.N. SURFRAD—A national surface radiation budget network for atmospheric research. Bull. Am. Meteorol. Soc.
**2000**, 81, 2341–2357. [Google Scholar] [CrossRef][Green Version] - Ndossi, M.; Avdan, U. Inversion of land surface temperature (LST) using terra ASTER data: A comparison of three algorithms. Remote Sens.
**2016**, 8, 993. [Google Scholar] [CrossRef][Green Version] - Li, S.; Yu, Y.; Sun, D.; Tarpley, D.; Zhan, X.; Chiu, L. Evaluation of 10 year AQUA/MODIS land surface temperature with SURFRAD observations. Int. J. Remote Sens.
**2014**, 35, 830–856. [Google Scholar] [CrossRef] - Heidinger, A.K.; Laszlo, I.; Molling, C.C.; Tarpley, D. Using SURFRAD to verify the NOAA single-channel land surface temperature algorithm. J. Atmos. Ocean. Technol.
**2013**, 30, 2868–2884. [Google Scholar] [CrossRef] - Liu, Y.; Yu, Y.; Yu, P.; Wang, H.; Rao, Y. Enterprise LST algorithm development and its evaluation with NOAA 20 data. Remote Sens.
**2019**, 11, 2003. [Google Scholar] [CrossRef][Green Version] - Freitas, S.C.; Trigo, I.; Macedo, J. GIO Global Land Component—Lot I “Operation of the Global Land Component”. Quality Assessment Report. 2015. Available online: https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/GIOGL1_VR_BAV1_I2.01.pdf (accessed on 1 December 2019).
- Jiménez, C.; Prigent, C.; Ermida, S.L.; Moncet, J.-L. Inversion of AMSR-E observations for land surface temperature estimation: 1. Methodology and evaluation with station temperature. J. Geophys. Res. Atmos.
**2017**, 122, 3330–3347. [Google Scholar] [CrossRef] - Sobrino, J.A.; Jiménez-Muñoz, J.C.; Paolini, L. Land surface temperature retrieval from Landsat TM 5. Remote Sens. Environ.
**2004**, 90, 434–440. [Google Scholar] [CrossRef] - Pedelty, J.; Devadiga, S.; Masuoka, E.; Brown, M.; Pinzon, J.; Tucker, C.; Vermote, E.; Prince, S.; Nagol, J.; Justice, C.; et al. Generating a long-term land data record from the AVHRR and MODIS instruments. In Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 23–28 July 2007; pp. 1021–1025. [Google Scholar]
- Coll, C.; Valor, E.; Galve, J.M.; Mira, M.; Bisquert, M.; García-Santos, V.; Caselles, E.; Caselles, V. Long-term accuracy assessment of land surface temperatures derived from the advanced along-track scanning radiometer. Remote Sens. Environ.
**2012**, 116, 211–225. [Google Scholar] [CrossRef] - Niclòs, R.; Galve, J.M.; Valiente, J.A.; Estrela, M.J.; Coll, C. Accuracy assessment of land surface temperature retrievals from MSG2-SEVIRI data. Remote Sens. Environ.
**2011**, 115, 2126–2140. [Google Scholar] [CrossRef] - Sun, D.; Pinker, R.T. Estimation of land surface temperature from a geostationary operational environmental satellite (GOES-8). J. Geophys. Res.
**2003**, 108, 4326. [Google Scholar] [CrossRef] - USGS. Landsat 8 OLI and TIRS Calibration Notices. Available online: https://www.usgs.gov/land-resources/nli/landsat/landsat-8-oli-and-tirs-calibration-notices (accessed on 24 July 2019).
- Li, S.; Jiang, G.-M. Land surface temperature retrieval from Landsat-8 data with the generalized split-window algorithm. IEEE Access
**2018**, 6, 18149–18162. [Google Scholar] [CrossRef] - Vlassova, L.; Perez-Cabello, F.; Nieto, H.; Martín, P.; Riaño, D.; de la Riva, J. Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling. Remote Sens.
**2014**, 6, 4345–4368. [Google Scholar] [CrossRef][Green Version] - Renard, F.; Alonso, L.; Fitts, Y.; Hadjiosif, A.; Comby, J. Evaluation of the effect of urban redevelopment on surface urban heat islands. Remote Sens.
**2019**, 11, 299. [Google Scholar] [CrossRef][Green Version] - Walawender, J.P.; Szymanowski, M.; Hajto, M.J.; Bokwa, A. Land surface temperature patterns in the urban agglomeration of Krakow (Poland) derived from Landsat-7/ETM+ data. Pure Appl. Geophys.
**2014**, 171, 913–940. [Google Scholar] [CrossRef][Green Version] - Peres, L.F.; DaCamara, C.C. Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI. IEEE Trans. Geosci. Remote Sens.
**2005**, 43, 1834–1844. [Google Scholar] [CrossRef] - Sobrino, J.A.; Raissouni, N. Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco. Int. J. Remote Sens.
**2000**, 21, 353–366. [Google Scholar] [CrossRef] - Valor, E.; Caselles, V. Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Remote Sens. Environ.
**1996**, 57, 167–184. [Google Scholar] [CrossRef] - Van de Griend, A.A.; Owe, M. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Int. J. Remote Sens.
**1993**, 14, 1119–1131. [Google Scholar] [CrossRef] - Snyder, W.C.; Wan, Z.; Zhang, Y.; Feng, Y.-Z. Classification-based emissivity for land surface temperature measurement from space. Int. J. Remote Sens.
**1998**, 19, 2753–2774. [Google Scholar] [CrossRef] - Sobrino, J.A.; El Kharraz, J.; Li, Z.-L. Surface temperature and water vapour retrieval from MODIS data. Int. J. Remote Sens.
**2003**, 24, 5161–5182. [Google Scholar] [CrossRef] - Cheng, J.; Liang, S. Estimating the broadband longwave emissivity of global bare soil from the MODIS shortwave albedo product. J. Geophys. Res. Atmos.
**2014**, 119, 614–634. [Google Scholar] [CrossRef] - Tang, B.-H.; Shao, K.; Li, Z.-L.; Wu, H.; Tang, R. An improved NDVI-based threshold method for estimating land surface emissivity using MODIS satellite data. Int. J. Remote Sens.
**2015**, 36, 4864–4878. [Google Scholar] [CrossRef] - Watson, K. Two-temperature method for measuring emissivity. Remote Sens. Environ.
**1992**, 42, 117–121. [Google Scholar] [CrossRef] - Peres, L.F.; DaCamara, C.C. Land surface temperature and emissivity estimation based on the two-temperature method: Sensitivity analysis using simulated MSG/SEVIRI data. Remote Sens. Environ.
**2004**, 91, 377–389. [Google Scholar] [CrossRef] - Peres, L.F.; Dacamara, C.C.; Trigo, I.F.; Freitas, S.C. Synergistic use of the two-temperature and split-window methods for land-surface temperature retrieval. Int. J. Remote Sens.
**2010**, 31, 4387–4409. [Google Scholar] [CrossRef] - Barducci, A.; Pippi, I. Temperature and emissivity retrieval from remotely sensed images using the “grey body emissivity” method. IEEE Trans. Geosci. Remote Sens.
**1996**, 34, 681–695. [Google Scholar] [CrossRef] - Borel, C.C. Iterative retrieval of surface emissivity and temperature for a hyperspectral sensor. In Proceedings of the JPL Workshop/Remote Sensing of Land Surface Emissivity, Pasadena, CA, USA, 6–8 May 1997. [Google Scholar]
- Borel, C. Error analysis for a temperature and emissivity retrieval algorithm for hyperspectral imaging data. Int. J. Remote Sens.
**2008**, 29, 5029–5045. [Google Scholar] [CrossRef] - Jaggi, S.; Quattrochi, D.; Baskin, R. An algorithm for the estimation of bounds on the emissivity and temperatures from thermal multispectral airborne remotely sensed data. In Proceedings of the Summaries of the Third Annual JPL Airborne Geoscience Workshop, Pasadena, CA, USA, 1–5 June 1992; pp. 22–24. [Google Scholar]
- Kahle, A.B.; Madura, D.P.; Soha, J.M. Middle infrared multispectral aircraft scanner data: Analysis for geological applications. Appl. Opt.
**1980**, 19, 2279–2290. [Google Scholar] [CrossRef] [PubMed] - Li, Z.; Petitcolin, F.; Renhua, Z. A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data. Sci. China Ser. E-Technol. Sci.
**2000**, 43, 23–33. [Google Scholar] [CrossRef] - Petitcolin, F.; Vermote, E. Land surface reflectance, emissivity and temperature from MODIS middle and thermal infrared data. Remote Sens. Environ.
**2002**, 83, 112–134. [Google Scholar] [CrossRef] - Jiang, G.-M.; Li, Z.-L.; Nerry, F. Land surface emissivity retrieval from combined mid-infrared and thermal infrared data of MSG-SEVIRI. Remote Sens. Environ.
**2006**, 105, 326–340. [Google Scholar] [CrossRef] - Wan, Z.; Li, Z.-L. A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Trans. Geosci. Remote Sens.
**1997**, 35, 980–996. [Google Scholar] [CrossRef] - Ma, X.L.; Wan, Z.; Moeller, C.C.; Menzel, W.P.; Gumley, L.E.; Zhang, Y. Retrieval of geophysical parameters from moderate resolution imaging spectroradiometer thermal infrared data: Evaluation of a two-step physical algorithm. Appl. Opt.
**2000**, 39, 3537–3550. [Google Scholar] [CrossRef] - Ma, X.L.; Wan, Z.; Moeller, C.C.; Menzel, W.P.; Gumley, L.E. Simultaneous retrieval of atmospheric profiles, land-surface temperature, and surface emissivity from moderate-resolution imaging spectroradiometer thermal infrared data: Extension of a two-step physical algorithm. Appl. Opt.
**2002**, 41, 909–924. [Google Scholar] [CrossRef] - Li, J.; Li, J.; Weisz, E.; Zhou, D.K. Physical retrieval of surface emissivity spectrum from hyperspectral infrared radiances. Geophys. Res. Lett.
**2007**, 34, 4–9. [Google Scholar] [CrossRef][Green Version] - Carlson, T.N.; Ripley, D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ.
**1997**, 62, 241–252. [Google Scholar] [CrossRef] - Wang, K.; Wan, Z.; Wang, P.; Sparrow, M.; Liu, J.; Zhou, X.; Haginoya, S. Estimation of surface long wave radiation and broadband emissivity using moderate resolution imaging spectroradiometer (MODIS) land surface temperature/emissivity products. J. Geophys. Res.
**2005**, 110, D11109. [Google Scholar] [CrossRef] - Wang, K.; Liang, S. Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites. Remote Sens. Environ.
**2009**, 113, 1556–1565. [Google Scholar] [CrossRef] - Tukey, J.W. Box-and-whisker plots. In Exploratory Data Analysis; Pearson: Reading, MA, USA, 1977; pp. 39–43. ISBN 0201076160. [Google Scholar]
- Sameen, M.I.; Kubaisy, M.A. Automatic surface temperature mapping in ArcGIS using Landsat-8 TIRS and ENVI tools case study: Al Habbaniyah Lake. J. Environ. Earth Sci.
**2014**, 4, 12–17. [Google Scholar] - Isaya Ndossi, M.; Avdan, U. Application of open source coding technologies in the production of land surface temperature (LST) maps from Landsat: A PyQGIS plugin. Remote Sens.
**2016**, 8, 413. [Google Scholar] [CrossRef][Green Version] - Walawender, J.P.; Hajto, M.J.; Iwaniuk, P. A new ArcGIS toolset for automated mapping of land surface temperature with the use of LANDSAT satellite data. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012; pp. 4371–4374. [Google Scholar]
- Zhang, J.; Wang, Y.; Li, Y. A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6. Comput. Geosci.
**2006**, 32, 1796–1805. [Google Scholar] [CrossRef] - Tardy, B.; Rivalland, V.; Huc, M.; Hagolle, O.; Marcq, S.; Boulet, G. A software tool for atmospheric correction and surface temperature estimation of Landsat infrared thermal data. Remote Sens.
**2016**, 8, 696. [Google Scholar] [CrossRef][Green Version] - Oguz, H. LST calculator: A program for retrieving land surface temperature from Landsat TM/ETM+ imagery. Environ. Eng. Manag. J.
**2013**, 12, 549–555. [Google Scholar] [CrossRef] - Sun, Q.; Tan, J.; Xu, Y. An ERDAS image processing method for retrieving LST and describing urban heat evolution: A case study in the Pearl River Delta Region in South China. Environ. Earth Sci.
**2009**, 59, 1047–1055. [Google Scholar] [CrossRef] - Oltra-Carrió, R.; Sobrino, J.A.; Franch, B.; Nerry, F. Land surface emissivity retrieval from airborne sensor over urban areas. Remote Sens. Environ.
**2012**, 123, 298–305. [Google Scholar] [CrossRef] - Neinavaz, E.; Skidmore, A.K.; Darvishzadeh, R. Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method. Int. J. Appl. Earth Obs. Geoinf.
**2020**, 85, 101984. [Google Scholar] [CrossRef] - Cao, B.; Liu, Q.; Du, Y.; Roujean, J.-L.; Gastellu-Etchegorry, J.-P.; Trigo, I.F.; Zhan, W.; Yu, Y.; Cheng, J.; Jacob, F.; et al. A review of earth surface thermal radiation directionality observing and modeling: Historical development, current status and perspectives. Remote Sens. Environ.
**2019**, 232, 111304. [Google Scholar] [CrossRef] - Trigo, I.F.; Monteiro, I.T.; Olesen, F.; Kabsch, E. An assessment of remotely sensed land surface temperature. J. Geophys. Res.
**2008**, 113, D17108. [Google Scholar] [CrossRef] - Zhou, J.; Li, M.; Liu, S.; Jia, Z.; Ma, Y. Validation and performance evaluations of methods for estimating land surface temperatures from ASTER data in the middle reach of the Heihe River Basin, Northwest China. Remote Sens.
**2015**, 7, 7126–7156. [Google Scholar] [CrossRef][Green Version] - Guillevic, P.; Göttsche, F.; Nickeson, J.; Hulley, G.; Ghent, D.; Yu, Y.; Trigo, I.; Hook, S.; Sobrino, J.A.; Remedios, J.; et al. Land Surface Temperature Product Validation Best Practice Protocol; Guillevic, P., Göttsche, F., Nickeson, J., Román, M., Eds.; Version 1.1; CEOS WGCV Land Product Validation Subgroup: Greenbelt, MD, USA, 2018. [Google Scholar]
- Hook, S.J.; Vaughan, R.G.; Tonooka, H.; Schladow, S.G. Absolute radiometric in-flight validation of mid infrared and thermal infrared data from ASTER and MODIS on the terra spacecraft using the Lake Tahoe, CA/NV, USA, automated validation site. IEEE Trans. Geosci. Remote Sens.
**2007**, 45, 1798–1807. [Google Scholar] [CrossRef] - Guillevic, P.C.; Biard, J.C.; Hulley, G.C.; Privette, J.L.; Hook, S.J.; Olioso, A.; Göttsche, F.M.; Radocinski, R.; Román, M.O.; Yu, Y.; et al. Validation of land surface temperature products derived from the visible infrared imaging radiometer suite (VIIRS) using ground-based and heritage satellite measurements. Remote Sens. Environ.
**2014**, 154, 19–37. [Google Scholar] [CrossRef] - Sobrino, J.; Skoković, D. Permanent stations for calibration/validation of thermal sensors over Spain. Data
**2016**, 1, 10. [Google Scholar] [CrossRef] - Göttsche, F.-M.; Olesen, F.-S.; Trigo, I.; Bork-Unkelbach, A.; Martin, M. Long term validation of land surface temperature retrieved from MSG/SEVIRI with continuous in-situ measurements in Africa. Remote Sens.
**2016**, 8, 410. [Google Scholar] [CrossRef][Green Version] - Emami, H.; Mojaradi, B.; Safari, A. A new approach for land surface emissivity estimation using LDCM data in semi-arid areas: Exploitation of the ASTER spectral library data set. Int. J. Remote Sens.
**2016**, 37, 5060–5085. [Google Scholar] [CrossRef] - Dozier, J.; Warren, S.G. Effect of viewing angle on the infrared brightness temperature of snow. Water Resour. Res.
**1982**, 18, 1424–1434. [Google Scholar] [CrossRef][Green Version] - USGS. Landsat 7 (L7) Data Users Handbook. Available online: https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1927_L7_Data_Users_Handbook-v2.pdf (accessed on 5 December 2019).
- Zanter, K. Landsat 8 (L8) Data Users Handbook; EROS: Sioux Falls, SD, USA, 2019. [Google Scholar]
- Barsi, J.A.; Barker, J.L.; Schott, J.R. An atmospheric correction parameter calculator for a single thermal band earth-sensing instrument. In Proceedings of the IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), Toulouse, France, 21–25 July 2003; pp. 3014–3016. [Google Scholar]
- Barsi, J.A.; Schott, J.R.; Palluconi, F.D.; Hook, S.J. Validation of a web-based atmospheric correction tool for single thermal band instruments. In Proceedings of the Earth Observing Systems X, San Diego, CA, USA, 31 July–4 August 2005; p. 58820E. [Google Scholar]
- Liu, L.; Zhang, Y. Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sens.
**2011**, 3, 1535–1552. [Google Scholar] [CrossRef][Green Version] - Wang, L.; Lu, Y.; Yao, Y. Comparison of three algorithms for the retrieval of land surface temperature from Landsat 8 images. Sensors
**2019**, 19, 5049. [Google Scholar] [CrossRef] [PubMed][Green Version] - Wang, F.; Qin, Z.; Song, C.; Tu, L.; Karnieli, A.; Zhao, S. An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sens.
**2015**, 7, 4268–4289. [Google Scholar] [CrossRef][Green Version] - Wang, H.; Mao, K.; Mu, F.; Shi, J.; Yang, J.; Li, Z.; Qin, Z. A split window algorithm for retrieving land surface temperature from FY-3D MERSI-2 data. Remote Sens.
**2019**, 11, 2083. [Google Scholar] [CrossRef][Green Version]

**Figure 1.**Accuracy assessment and the comparison of method-based LST results with ground-based LST: (

**a**) Comparison between Mono Window Algorithm (MWA)-based LST and LST

_{SURFRAD}, (

**b**) Comparison between Radiative Transfer Equation (RTE)-based LST and LST

_{SURFRAD}, (

**c**) Comparison between Single Channel Algorithm (SCA)-based LST and LST

_{SURFRAD}.

**Figure 2.**Landsat 8 LST image (27 April 2018) over the 6 × 6 km

^{2}rural area covering the BND station, for the three methods used in this analysis: (

**a**) MWA-based LST, (

**b**) RTE-based LST, (

**c**) SCA-based LST. The coordinate system, projection and zone information of maps are the World Geodetic System 1984 (WGS84), Universal Transverse Mercator (UTM) Projection, and Zone 16 N, respectively.

**Figure 3.**Seasonal RMS differences between the LST retrieval methods. Box-plot information: minimum (1), first quartile (2), median (3), third quartile (4), and maximum (5). The cross “x” in the boxes is the mean value of the data set.

**Table 1.**Information about the Surface Radiation Budget Network (SURFRAD) experimental sites used in the study.

Site Name | Site Code | Latitude | Longitude | Elevation | Land Cover Type |
---|---|---|---|---|---|

Bondville, Illinois | BND | 40.05° N | 88.37° W | 230 m | Cropland |

Desert Rock, Nevada | DRA | 36.62° N | 116.02° W | 1007 m | Open Shrub-lands |

Fort Peck, Montana | FPK | 48.31° N | 105.10° W | 634 m | Grassland |

Goodwin Creek, Mississippi | GWN | 34.26° N | 89.87° W | 98 m | Cropland/Natural Vegetation Mosaic |

Penn. State Univ., Pennsylvania | PSU | 40.72° N | 77.93° W | 376 m | Cropland |

TIR Bands | Range | a | B (K) |
---|---|---|---|

Band 10 | −10–20 °C | 0.4087 | −55.58 |

20–50 °C | 0.4464 | −66.61 | |

Band 11 | −10–20 °C | 0.4442 | −59.85 |

20–50 °C | 0.4831 | −71.23 |

**Table 3.**The state-of-art table showing different LSE categories, LSE models, and the corresponding satellite missions used.

Category | Surface Emissivity Determination Methods | References | Platform |
---|---|---|---|

Semi-Empirical Methods (SEMs) | Classification-based emissivity method (CBEM) | [80] | MSG1/SEVIRI |

[84] | MODIS | ||

NDVI-based emissivity method (NBEM) | [83] | NOAA/AVHRRLandsat TM | |

[82] | NOAA/AVHRRLandsat TM | ||

[81] | NOAA/AVHRR | ||

[85] | TERRA/MODIS | ||

[58] | ENVISAT/AATSR MSG1/SEVIRI Landsat TM | ||

[60] | Landsat 8 | ||

[46] | Landsat 8 | ||

[86] | MODIS | ||

[87] | TERRA/MODIS | ||

[76] | Landsat 8 | ||

Multi-channel TES methods | The two-temperature method (TTM) | [88] | TIMS |

[89] | MSG/SEVIRI | ||

[90] | MSG/SEVIRI | ||

Grey-body emissivity (GBE) method | [91] | TIMS | |

The iterative spectrally smooth temperature emissivity separation (ISSTES) method | [92,93] | Hyperspectral infrared data | |

The emissivity bounds method (EBM) | [94] | TIMS | |

Reference channel method (RCM) | [95] | multispectral aircraft scanner data | |

TES method | [40] | ASTER | |

[58] | ASTER Airborne Hyperspectral Scanner (AHS) | ||

Temperature-independent spectral indices (TISI) based methods | [33] | NOAA/AVHRR | |

[96] | NOAA/AVHRR | ||

[97] | TERRA/MODIS | ||

[98] | MSG-SEVIRI | ||

Physically-based methods (PBMs) | Physics-based day/night (D/N) method | [99] | TERRA/MODIS |

Two-step physical retrieval method (TSRM) | [100,101] | TERRA/MODIS | |

[102] | AQUA/AIRS |

**Table 4.**The expressions of Normalized Difference Vegetation Index (NDVI) threshold models used in this study.

Sensor | LSE Equations | Reference |
---|---|---|

Landsat 5 TM and 7 ETM+ (Band 6) | $\mathsf{\epsilon}=\{\begin{array}{cc}0.979-0.035{\mathsf{\rho}}_{\mathrm{R}}\hfill & \hfill \mathrm{NDVI}<0.2\\ 0.004{\mathrm{P}}_{\mathrm{v}}+0.986\hfill & \hfill 0.2\le \mathrm{NDVI}\le 0.5\\ 0.99\hfill & \hfill \mathrm{NDVI}>0.5\end{array}$ | Sobrino et al. [58] |

Landsat 8 TIR1 (Band 10) | $\mathsf{\epsilon}=\{\begin{array}{cc}0.979-0.046{\mathsf{\rho}}_{\mathrm{R}}\hfill & \hfill \mathrm{NDVI}<0.2\\ 0.987{\mathrm{P}}_{\mathrm{v}}+0.971(1-{\mathrm{P}}_{\mathrm{v}})+\mathrm{d}\mathsf{\epsilon}\hfill & \hfill 0.2\le \mathrm{NDVI}\le 0.5\\ 0.987+\mathrm{d}\mathsf{\epsilon}\hfill & \hfill \mathrm{NDVI}>0.5\end{array}$ | Skoković et al. [60] |

Landsat 8 TIR1 (Band 11) | $\mathsf{\epsilon}=\{\begin{array}{cc}0.982-0.027{\mathsf{\rho}}_{\mathrm{R}}\hfill & \hfill \mathrm{NDVI}<0.2\\ 0.989{\mathrm{P}}_{\mathrm{v}}+0.977(1-{\mathrm{P}}_{\mathrm{v}})+\mathrm{d}\mathsf{\epsilon}\hfill & \hfill 0.2\le \mathrm{NDVI}\le 0.5\\ 0.989+\mathrm{d}\mathsf{\epsilon}\hfill & \hfill \mathrm{NDVI}>0.5\end{array}$ | Skoković et al. [60] |

Landsat 8 TIR1 (Band 10) | $\mathsf{\epsilon}=\{\begin{array}{cc}0.973-0.047{\mathsf{\rho}}_{\mathrm{R}}\hfill & \hfill \mathrm{NDVI}<0.2\\ 0.9863{\mathrm{P}}_{\mathrm{v}}+0.9668(1-{\mathrm{P}}_{\mathrm{v}})+\mathrm{d}\mathsf{\epsilon}\hfill & \hfill 0.2\le \mathrm{NDVI}\le 0.5\\ 0.9863+\mathrm{d}\mathsf{\epsilon}\hfill & \hfill \mathrm{NDVI}>0.5\end{array}$ | Yu et al. [46] |

Landsat 8 TIR1 (Band 11) | $\mathsf{\epsilon}=\{\begin{array}{cc}0.984-0.0026{\mathsf{\rho}}_{\mathrm{R}}\hfill & \hfill \mathrm{NDVI}<0.2\\ 0.9896{\mathrm{P}}_{\mathrm{v}}+0.9747(1-{\mathrm{P}}_{\mathrm{v}})+\mathrm{d}\mathsf{\epsilon}\hfill & \hfill 0.2\le \mathrm{NDVI}\le 0.5\\ 0.9896+\mathrm{d}\mathsf{\epsilon}\hfill & \hfill \mathrm{NDVI}>0.5\end{array}$ | Yu et al. [46] |

Landsat 8 TIR1 (Band 10) | $\mathsf{\epsilon}=\{\begin{array}{cc}{\mathrm{a}}_{\mathrm{l}\mathrm{i}}+{\displaystyle \sum _{\mathrm{j}=2}^{7}}{\mathrm{a}}_{\mathrm{j}\mathrm{i}}{\mathsf{\rho}}_{\mathrm{j}}\hfill & \hfill \mathrm{NDVI}<0.2\\ 0.982{\mathrm{P}}_{\mathrm{v}}+0.971(1-{\mathrm{P}}_{\mathrm{v}})+\mathrm{d}\mathsf{\epsilon}\hfill & \hfill 0.2\le \mathrm{NDVI}\le 0.5\\ 0.982+\mathrm{d}\mathsf{\epsilon}\hfill & \hfill \mathrm{NDVI}>0.5\end{array}$ | Li and Jiang [76] |

Landsat 8 TIR1 (Band 11) | $\mathsf{\epsilon}=\{\begin{array}{cc}{\mathrm{a}}_{\mathrm{l}\mathrm{i}}+{\displaystyle \sum _{\mathrm{j}=2}^{7}}{\mathrm{a}}_{\mathrm{j}\mathrm{i}}{\mathsf{\rho}}_{\mathrm{j}}\hfill & \hfill \mathrm{NDVI}<0.2\\ 0.984{\mathrm{P}}_{\mathrm{v}}+0.976(1-{\mathrm{P}}_{\mathrm{v}})+\mathrm{d}\mathsf{\epsilon}\hfill & \hfill 0.2\le \mathrm{NDVI}\le 0.5\\ 0.984+\mathrm{d}\mathsf{\epsilon}\hfill & \hfill \mathrm{NDVI}>0.5\end{array}$ | Li and Jiang [76] |

**Table 5.**Validation results of the Land Surface Temperature (LST) retrieval methods for Landsat 5 Thematic Mapper (TM) data based on different Land Surface Emissivity (LSE) models. The best result is in bold.

Landsat Mission | Emissivity Method | LST Retrieval Method | RMSE (K) |
---|---|---|---|

Landsat 5 TM | Van De Griend & Owe (1993) | MWA | 4.89 |

RTE | 4.96 | ||

SCA | 5.22 | ||

Valor & Caselles (1996) | MWA | 2.93 | |

RTE | 3.25 | ||

SCA | 3.46 | ||

Sobrino et al. (2008) | MWA | 2.41 | |

RTE | 2.35 | ||

SCA | 2.47 |

**Table 6.**Validation results of LST retrieval methods for Landsat 7 ETM+ data based on different LSE models. The best result is in bold.

Landsat Mission | Emissivity Method | LST Retrieval Method | RMSE (K) |
---|---|---|---|

Landsat 7 ETM+ | Van De Griend & Owe (1993) | MWA | 9.10 |

RTE | 8.18 | ||

SCA | 9.51 | ||

Valor & Caselles (1996) | MWA | 4.64 | |

RTE | 4.95 | ||

SCA | 5.25 | ||

Sobrino et al. (2008) | MWA | 2.24 | |

RTE | 2.48 | ||

SCA | 2.77 |

**Table 7.**Validation results of LST retrieval methods for Landsat 8 OLI/TIRS data based on different LSE models. The best result is in bold.

Landsat Mission | Emissivity Method | LST Retrieval Method | RMSE (K) |
---|---|---|---|

Landsat 8 OLI/TIRS | VanDeGriend & Owe (1993) | MWA | 4.24 |

RTE | 4.28 | ||

SCA | 4.53 | ||

Valor & Caselles (1996) | MWA | 5.16 | |

RTE | 4.21 | ||

SCA | 5.11 | ||

Sobrino et al. (2008) | MWA | 2.52 | |

RTE | 2.85 | ||

SCA | 2.94 | ||

Skoković et al. (2014) | MWA | 2.73 | |

RTE | 3.01 | ||

SCA | 3.11 | ||

SWA | 2.79 | ||

Yu et al. (2014) | MWA | 2.79 | |

RTE | 3.07 | ||

SCA | 3.18 | ||

SWA | 3.02 | ||

Li & Jiang (2018) | MWA | 2.85 | |

RTE | 3.11 | ||

SCA | 3.22 | ||

SWA | 2.94 |

Quantity | Uncertainty | Estimated Impact on Ground-Based LST |
---|---|---|

Radiometric Calibration | ± 0.2 to 0.5 K | 0.2 K |

Emissivity | ± 1% | 0.3 K |

Downwelling atmospheric radiance | ± 10% | 0.1 K |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Sekertekin, A.; Bonafoni, S. Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation. *Remote Sens.* **2020**, *12*, 294.
https://doi.org/10.3390/rs12020294

**AMA Style**

Sekertekin A, Bonafoni S. Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation. *Remote Sensing*. 2020; 12(2):294.
https://doi.org/10.3390/rs12020294

**Chicago/Turabian Style**

Sekertekin, Aliihsan, and Stefania Bonafoni. 2020. "Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation" *Remote Sensing* 12, no. 2: 294.
https://doi.org/10.3390/rs12020294