Land surface temperature retrieval from Landsat 8 data and validation with geosensor network

doi:10.1007/s11707-016-0570-7

Front. Earth Sci.

2017, Vol. 11

Issue (1) : 20-34 https://doi.org/10.1007/s11707-016-0570-7

RESEARCH ARTICLE

Land surface temperature retrieval from Landsat 8 data and validation with geosensor network

Kun TAN¹,Zhihong LIAO¹,Peijun DU²(

),Lixin WU¹

¹. Jiangsu Key Laboratory of Resources and Environment Information Engineering, China University of Mining and Technology, Xuzhou 221116, China
². Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China

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Abstract

A method for the retrieval of land surface temperature (LST) from the two thermal bands of Landsat 8 data is proposed in this paper. The emissivities of vegetation, bare land, buildings, and water are estimated using different features of the wavelength ranges and spectral response functions. Based on the Planck function of the Thermal Infrared Sensor (TIRS) band 10 and band 11, the radiative transfer equation is rebuilt and the LST is obtained using the modified emissivity parameters. A sensitivity analysis for the LST retrieval is also conducted. The LST was retrieved from Landsat 8 data for the city of Zoucheng, Shandong Province, China, using the proposed algorithm, and the LST reference data were obtained at the same time from a geosensor network (GSN). A comparative analysis was conducted between the retrieved LST and the reference data from the GSN. The results showed that water had a higher LST error than the other land-cover types, of less than 1.2°C, and the LST errors for buildings and vegetation were less than 0.75°C. The difference between the retrieved LST and reference data was about 1°C on a clear day. These results confirm that the proposed algorithm is effective for the retrieval of LST from the Landsat 8 thermal bands, and a GSN is an effective way to validate and improve the performance of LST retrieval.

Keywords Land surface temperature (LST) split-window algorithm emissivity Landsat 8

Corresponding Author(s): Peijun DU

Just Accepted Date: 05 May 2016 Online First Date: 08 June 2016 Issue Date: 23 January 2017

Cite this article:

Kun TAN,Zhihong LIAO,Peijun DU, et al. Land surface temperature retrieval from Landsat 8 data and validation with geosensor network[J]. Front. Earth Sci., 2017, 11(1): 20-34.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-016-0570-7
https://academic.hep.com.cn/fesci/EN/Y2017/V11/I1/20

Fig.1 Relationship between band 10 and band 11. (a) Spectral response functions, (b) Change of the Planck radiance

B 10 (T)

and

B 11 (T)

with temperature.

Temperature range/°C	Parameter $a$	Parameter $b$	$R 2$
-20?0	0.896989	0.438607	0.999983
-10?10	0.883203	0.516232	0.999985
0?20	0.870708	0.600351	0.999987
0?50	0.853304	0.732458	0.999940
0?70	0.842858	0.824517	0.999901

Tab.1 Linear regression results of

B 10 (T)

and

B 11 (T)

Tab.2 The typical land surface emissivities of Landsat 8 TIRS

Fig.2 The emissivity of typical land surfaces.

Temperature range/°C	Parameter $a 10$	Parameter $b 10$	$R 102$	Parameter $a 11$	Parameter $b 11$	$R 112$
- 20?0	-21.051139	0.099695	0.999105	-18.377986	0.089182	0.999339
-10?10	-24.198012	0.111409	0.999260	- 20.776336	0.098109	0.999455
0?20	-27.166567	0.122081	0.999373	-18.377986	0.089182	0.999339
0?50	-32.253872	0.139974	0.996991	-14.901314	0.075912	0.994441
0?70	-35.721226	0.151823	0.995052	-12.722793	0.067324	0.986853

Tab.3 Linear regression results of

B i (T i)

and

T i

Fig.3 Errors in LST due to the errors in the land surface emissivity of band 11, with the emissivity of band 10 increasing.

Fig.4 Errors in LST due to the errors in the land surface emissivity of band 11, with the brightness temperature increasing.

Fig.5 Errors in LST due to the errors in the land surface emissivity of bands 10 and 11, with the emissivity increasing.

Fig.6 Errors in LST due to the errors in the land surface emissivity of bands 10 and 11, with the brightness temperature increasing.

Fig.7 Errors in the LST due to errors in the atmospheric transmittance of band 11.

Fig.8 Errors in the LST due to errors in the atmospheric transmittance of bands 10 and 11.

Fig.9 Experiments with data from July 24, 2013. (a) The false-color composite image. (b) The classification image. (c) Emissivity of band 10. (d) Emissivity of band 11.

Fig.10 Experiments with data from September 26, 2013. (a) The false-color composite image. (b) The classification image. (c) Emissivity of band 10. (d) Emissivity of band 11.

Fig.11 Location of the temperature sensors.

Fig.12 Wireless temperature sensors.

Fig.13 LST on July 24, 2013. (a) The LST retrieved by the proposed split-window algorithm. (b) The LST retrieved by Mao’s algorithm. (c) The difference image between (a) and (b). (d) The LST retrieved by Qin’s algorithm. (e) The difference image between (a) and (d).

Fig.14 The LST of Vegetation1 on July 24, 2013.

Date	Land-surface type	$LST GSN$	$LST proposed$	$Δ LST GSN-proposed$	$LST Mao$	$Δ LST GSN-Mao$	$LST Qin$	$Δ LST GSN-Qin$
20130724	Water1	28.4	29.254	?0.854	29.382	?0.982	28.784	?0.384
	Water2	27.8	28.714	?0.914	28.901	?1.101	28.241	?0.441
	Vegetation	30.823	31.052	?0.229	30.859	0.765	30.426	0.397
	Building	37.130	37.517	?0.387	37.954	?0.824	36.229	0.901
20130926	Water1	21.6	22.217	?0.617	22.095	?0.495	21.974	?0.374
	Water2	20.4	21.544	?1.144	21.682	?1.282	21.478	?1.078
	Vegetation	28.722	29.323	?0.601	28.903	?0.181	28.572	?0.15
	Building	32.805	33.507	?0.702	32.526	0.279	32.573	0.232

Tab.4 Results from the ground survey and LST images

Fig.15 LST on September 26, 2013. (a) The LST retrieved by the proposed split-window algorithm. (b) The LST retrieved by Mao’s algorithm. (c) The difference image between (a) and (b). (d) The LST retrieved by Qin’s algorithm. (e) The difference image between (a) and (d).

Fig.16 The LST of Vegetation1 on September 26, 2013.

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