Plugin QGIS SPT

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Concepts

Plugin QGIS SPT provides you for estimating land surface temperature (LST) using split-window algorithm (SWA) Qin for Landsat 8.

The steps to using Plugin QGIS SPT are fairly simple:

  1. Open the plugin QGIS SPT from within QGIS.

  2. Fill out the required value and input data band obtained from landsat 8 satellite imagery.

  3. If you have the different value, you can change the default value from advance setting.

  4. Calculate LST.

Running Plugin QGIS SPT

Tab Parameter

General Setting:

images/Parameter.png
Data Satellite

Data Satellite is data with values ​​obtained from the metadata used for calculate LST , see Table Metadata Landsat 8.

Output Temp.

Output temperature is to determine the external value of the resulting temperature , see Temperature Conversion.

Range Temp.

Range temperature is the average value of air temperature in the area , see Table Regression Coefficients.

Tot. Water Vapor

Total water vapor is the value of total water vapor content , see Total Water Vapor Content.

Band Red

Band red is data from landsat 8 satellite imagery , see Table band Red, NIR, TIR-1, and TIR-2 Landsat 8.

Band NIR

Band near-infrared (NIR) is data from landsat 8 satellite imagery , see Table band Red, NIR, TIR-1, and TIR-2 Landsat 8.

Band TIR-1

Band thermal-infrared 1 (TIR-1) is data from landsat 8 satellite imagery , see Table band Red, NIR, TIR-1, and TIR-2 Landsat 8.

Band TIR-2

Band thermal-infrared 2 (TIR-2) is data from landsat 8 satellite imagery , see Table band Red, NIR, TIR-1, and TIR-2 Landsat 8.

Save Output

Save output is to determine the external directory of the data LST.

Advance Setting:

images/ParameterAdv.png
At-Transmittance

This is the value of atmospheric transmittance , see Atmospheric Transmittance.

NDVI Soil

This is the value of NDVI soil , see Table NDVI Soil and Vegetation.

NDVI Vegetation

This is the value of NDVI vegetation , see Table NDVI Soil and Vegetation.

TIR-1 Emis. Soil

This is the value of TIR-1 emissivity soil , see Table Emissivity Soil and Vegetation.

TIR-1 Emis. Veg.

This is the value of TIR-1 emissivity vegetation , see Table Emissivity Soil and Vegetation.

TIR-2 Emis. Soil

This is the value of TIR-2 emissivity soil , see Table Emissivity Soil and Vegetation.

TIR-2 Emis. Veg.

This is the value of TIR-2 emissivity vegetation , see Table Emissivity Soil and Vegetation.

Geomectical

This is the value of geomectical factor , see Table Geometrical Factor.

Tab About

images/About.png

This is a description of the plugin QGIS SPT (author, version plugin, etc.).

Result

images/Result.png

This is a log generated from calculating LST.

Other

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Status:

This is a text to shows the activity of plugin QGIS SPT.

Button Ok

This is the button to start the process of calculating LST , see Land Surface Temperature (LST) Split-Window Algorithm Qin.

Button Close

This is the button to close the plugin QGIS SPT.

Button Help

This is the button to display the help page.

Method Land Surface Temperature (LST)

Equation

Land Surface Temperature (LST) Split-Window Algorithm Qin : 1

(1)\[T_{s}=A_{0}+A_{1} T_{10}-A_{2} T_{11}\]
(2)\[A_{0}=E_{1} a_{10}+E_{2} a_{11}\]
(3)\[A_{1}=1+A+E_{1} b_{10}\]
(4)\[A_{2}=A+E_{2} b_{11}\]
(5)\[A=D_{10} / E_{0}\]
(6)\[E_{0}=D_{11} C_{10}-D_{10} C_{11}\]
(7)\[E_{1}=D_{11}\left(1-C_{10}-D_{10}\right) / E_{0}\]
(8)\[E_{2}=D_{10}\left(1-C_{11}-D_{11}\right) / E_{0}\]
(9)\[D_{i}=\left[1-\tau_{i}(\theta)\right]\left[1+\left(1-\varepsilon_{i}\right) \tau_{i}(\theta)\right]\]
(10)\[C_{i}=\varepsilon_{i} \tau_{i}(\theta)\]

Top Of Atmospheric Brightness Temperature : 3

(11)\[T_{i}=\frac{K_{2}}{\ln \left(\frac{K_{1}}{L_{\lambda}}+1\right)}\]

Atmospheric Transmittance : 1

(12)\[\tau_{10}=-0.1146 w+1.0286\]
(13)\[\tau_{11}=-0.1568 w+1.0083\]
(14)\[\tau_{10}=-0.1134 w+1.0335\]
(15)\[\tau_{11}=-0.1546 w+1.0078\]

Spectral Radiance : 3

(16)\[L_{\lambda}=M_{L} * Q_{c a l}+A_{L}\]

Land Surface Emissivity : 4

(17)\[\begin{split}\varepsilon_{i}=\left\{\begin{array}{cl} \varepsilon_{s \lambda i} & NDVI<NDVI_{s} \\ \varepsilon_{v \lambda i} P_{v}+\varepsilon_{s \lambda i}\left(1-P_ {v}\right)+C_{\lambda i}, & NDVI_{s} \leq NDVI \leq NDVI_{v} \\ \varepsilon_{v \lambda i} P_{v}+C_{\lambda i,} & NDVI>NDVI_{v} \end{array}\right.\end{split}\]

Surface Roughness : 4

(18)\[C_{\lambda i}=\left(1-\varepsilon_{s \lambda i}\right) \varepsilon_{v \lambda i} F^{\prime}\left(1-P_{v}\right)\]

Proportion of Vegetation : 4

(19)\[P_{v}=\left[\frac{NDVI-NDVI_{s}}{NDVI_{v}-NDVI_{s}}\right]^{2}\]

Normalized Difference Vegetation Index (NDVI) : 4

(20)\[NDVI=\frac{N I R-R}{N I R+R}\]

Total Water Vapor Content : 4

(21)\[\omega=\omega(0) / R_{\omega}(0)\]
(22)\[\omega(0)=H \times E \times a / 1000\]

Temperature Conversion : 6

(23)\[\mathrm{K} \text { to }^{\circ} \mathrm{C}=0 \mathrm{K}-273.15\]
(24)\[^{\circ} \mathrm{C} \text { to } \mathrm{K}=0^{\circ} \mathrm{C}+273.15\]
(25)\[^{\circ} \mathrm{C} \text { to }^{\circ} \mathrm{F}=\left(0^{\circ} \mathrm{C} \times 9 / 5\right)+32\]

Value

Table Emissivity Soil and Vegetation : 4

Bands

\(\boldsymbol{\varepsilon_{s \lambda i}}\)

\(\boldsymbol{\varepsilon_{v \lambda i}}\)

Band TIR-1

0.964

0.984

Band TIR-2

0.970

0.980

Table Regression Coefficients : 1

T Range

\(\boldsymbol{a_{10}}\)

\(\boldsymbol{b_{10}}\)

\(\boldsymbol{a_{11}}\)

\(\boldsymbol{b_{11}}\)

0 - 30

-59.1391

0.4213

-63.3921

0.4565

0 - 40

-60.9196

0.4276

-65.2240

0.4629

10 - 10

-62.8065

0.4338

-67.1728

0.4694

10 - 50

-64.6081

0.4399

-69.0215

0.4756

Table Ratio Water Vapor Content for range value Total Water Vapor Content 0.5–3 g/cm\(\boldsymbol{^2 }\) : 2

T Range

\(\boldsymbol{R_{\omega}(0)}\)

Tropical atmosphere

0.6834

Sub-tropical summer

0.6819

Sub-tropical winter

0.6593

Mid-latitude summer

0.6834

Mid-latitude winter

0.6356

Table Geometrical Factor : 4

Description

\(\boldsymbol{F^{\prime}}\) Default Value

Geometrical Factor

0.5

Table NDVI Soil and Vegetation : 4

Description

\(\boldsymbol{NDVI_{s}}\) and \(\boldsymbol{NDVI_{v}}\) Default Value

NDVI Soil

0.2

NDVI Vegetation

0.5 (may be to low in some cases)

Table Saturation Mix Ratio and Air Density : 2

\(\boldsymbol{T}\) Range( \(\boldsymbol{^{\circ} \mathrm{C}}\) )

\(\boldsymbol{E}\) (g/kg \(\boldsymbol{^{-1}}\) )

\(\boldsymbol{a}\) (kg/m \(\boldsymbol{^{-3}}\) )

45

66.33

1.11

40

49.81

1.13

35

37.25

1.15

30

27.69

1.17

25

20.44

1.18

20

14.95

1.21

15

10.83

1.23

10

7.76

1.25

5

5.50

1.27

0

3.84

1.29

-5

2.52

1.32

-10

1.63

1.34

Table band Red, NIR, TIR-1, and TIR-2 Landsat 8 : 5

Bands

Bands Landsat 8

Band Red

Band 4

Band NIR

Band 5

Band TIR-1

Band 10

Band TIR-2

Band 11

Table Metadata Landsat 8 :

Metadata

Value Landsat 8

\(\boldsymbol{\boldsymbol{K}_{1}}\) (TIR-1)

774.8853

\(\boldsymbol{\boldsymbol{K}_{1}}\) (TIR-2)

480.8883

\(\boldsymbol{\boldsymbol{K}_{2}}\) (TIR-1)

1321.0789

\(\boldsymbol{\boldsymbol{K}_{2}}\) (TIR-2)

1201.1442

\(\boldsymbol{\boldsymbol{M}_{L}}\) (TIR-1 and TIR-2)

0.0003342

\(\boldsymbol{\boldsymbol{A}_{L}}\) (TIR-1 and TIR-2)

0.1

References

1(1,2,3)

Qin, et al. (2014) ‘Derivation of land surface temperature for landsat-8 TIRS using a split window algorithm’, Sensors (Switzerland), 14(4), pp. 5768–5780. doi: 10.3390/s140405768.

2(1,2)

Qin, et al. (2015) ‘An improved mono-window algorithm for land surface temperature retrieval from landsat 8 thermal infrared sensor data’, Remote Sensing, 7(4), pp. 4268–4289. doi: 10.3390/rs70404268.

3(1,2)

USGS. (2019) ‘Landsat 8 (L8) Data Users Handbook’.

4(1,2,3,4,5,6,7,8)

Tsou, J. et al. (2017) ‘Urban Heat Island Assessment Using the Landsat 8 Data: A Case Study in Shenzhen and Hong Kong’, Urban Science, 1(1), p. 10. doi: 10.3390/urbansci1010010.

5

https://landsat.gsfc.nasa.gov/wp-content/uploads/2013/01/BandpassesL7vL8_Jul20131.pdf

6

https://en.wikipedia.org/wiki/Conversion_of_units_of_temperature