2017 International Conference on Computer, Electronics and Communication Engineering (CECE 2017) ISBN: 978-1-60595-476-9

Retrieval of Land Surface Temperature from Landsat 8 for the Main Urban Area of

Meng-zhu SUN1,2, Kun YANG1,3 and Jia-sheng WANG1,3* 1The Engineering Research Center of GIS Technology in Western of Ministry of China, Kunming 650500, China 2School of Tourism and Geographical Science, Normal University, Kunming 650500, China 3School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China *Corresponding author

Keywords: Landsat 8, Kunming, Land surface temperature, Radiative transfer equation.

Abstract. Based on Landsat 8 TIRS 10 data, using the radiative transfer equation (RTE) and the improved mono-window algorithm (TIRS_SC) to retrieve the land surface temperature (LST) for the main urban area of Kunming, and compare the accuracy of the two methods. Then use the high precision method to retrieve LST of the main urban area of Kunming in 2013-2015. Results showed that RTE is more suitable for retrieval LST in the main urban area of Kunming. The LST of most area in Wuhua and is higher, and the low LST is mainly the and other region where there is water.

Introduction Land Surface Temperature (LST) is a key parameter in the analysis and simulation of land surface physical processes at the regional and the global scale; it’s also an important parameter in the global physical energy and water balance [1]. It has an important application in disaster monitoring and prevention, and the research of urban heat island effect etc. [2-4]. Landsat data is used in retrieval of LST by many people with high resolution, a large covered range and free. TM data has only one thermal infrared band. It usually used RTE, mono-window algorithm [5] and single-channel algorithm [6, 7] to retrieve LST [8, 9]. Landsat 8 data has two thermal infrared bands. It can be used in split window algorithm to retrieve LST but the result is not so good [10]. In this paper, RTE and TIRS_SC are used to retrieve LST for the main urban area of Kunming. Then use the high precision method to retrieve LST of the main urban area of Kunming in 2013-2015. It has a certain guiding significance for the department to formulate sustainable planning.

LST Retrieval Algorithm Radiative Transfer Equation (RTE) Radiative transfer equation (RTE) is one of the earliest developed algorithms. It can be widely used in any thermal infrared sensor data and it can be written as

1↓↑ (1) where is radiance received by the sensor; ε is land surface emissivity (LSE); is the true -2 -1 temperature of surface (K); is the ground radiance (W∙m ∙sr ∙μm); is atmospheric transmittance; ↑ is up-welling path radiance; ↓ is down-welling path radiance. B() can be derived from Eq. 1 written as

↑ 1↓/τε (2) in which , ↑ and ↓ can be solved from MODTRAN and other ways. Then the true temperature of the surface can be solved using the inverse function of Plank’s law, it can be expressed as

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/ 1 (3) -2 -1 in which K1 and K2 are constants, for Landsat8 the TIRS_10 band, K1 is 774.89 W∙m ∙sr ∙μm, K2 is 1321.08K. Mono-window Algorithm (MV) Momo-window algorithm (MV) was proposed by Qin [8] in 2001. It’s a LST retrieval algorithm for TM data with only one thermal infrared band. The MV algorithm does not require atmospheric correction. It is simple and easy for application due to it included the impact of atmosphere and surface. In this study, we use the improved mono-window algorithm (TIRS_SC) [11] to retrieve LST for the main urban area of Kunming. It can be written as

1 / (4) where ; 1 1 1 ; is the brightness temperature of TIRS 10; is a constant (1321.08K); is the average temperature of the atmosphere (K).

LST Retrieval in Main Urban Area of Kunming Study Area and Data Used Study Area. Kunming is in the middle of the Yunnan-Guizhou Plateau and it is one of the important central city in Western China (figure. 1). The most area of Dian Lake is included in and Xishan District, so we considered the whole Dian Lake as a study area.

Figure 1. Location of study area in Kunming, Yunnan.

Data Used. We collected the available Landsat 8 images which were acquired in the main urban area of Kunming in 2013 to 2015. The thermal infrared band TIRS_10 was used to estimate the ground radiance. The infrared and near infrared bands were used to calculate the normalized differential vegetation index (NDVI). We obtained temperature, pressure and relative humidity in the historical meteorological data sharing website (https://www.wunderground.com/).

681 Brightness Temperature Retrieval Using Planck inverse function retrieves the temperature of earth with the radiation intensity in the upper atmosphere which is converted by DN value with scaling factor. Brightness temperature can be expressed as:

/ln 1 (5) in which K1 and K2 are constants; is the radiation intensity in the upper atmosphere which can be calculated by radiation calibration. Estimation of Land Surface Emissivity Land surface emissivity (LSE) is one of the important parameters for the retrieval of LST and it is the generic parameter of different LST retrieval algorithms [12]. LSE has a great influence on the retrieval accuracy of LST. There are two major kinds of LSE retrieval methods: the experience method and mixed pixel method. In this study, we use the mixed pixel method proposed by Qin [5] to estimate the LSE. The surface of the urban is simply regarded as construction land, vegetation and water, so the LSE can be expressed as:

1 (6) where is the LSE of mixed pixels; is vegetation fraction; is temperature ratio of vegetation, and is temperature ratio of building surfaces; is the LSE of vegetation and is the LSE of building surfaces; is a radiation correction term. According to the characteristics of the Landsat 8 commonly used for terrestrial emissivity spectrum database provided by ASTER, for the TIRS_10 band, =0.98672, =0.96767 [13]. For , and , they can be estimated according to the formula proposed by Qin [14]. is related to the proportion of vegetation. They can be expressed as:

Rv=0.9332+0.0585Pv (7)

Rm=0.9886+0.1287Pv (8) 0.0038P P 0.5 v v (9) 0.00381Pv Pv 0.5 Vegetation fraction can be calculated as:

(10) in which NDVI is normalized differential vegetation index; is the NDVI of vegetation and is NDVI of bare land, take the experience value =0.5, =0.05; and if NDVI>0.5, =1; if NDVI<0.05, =0. Estimation of Other Parameters Table 1. Estimation equation of average atmospheric temperature. Atmospheric Model Estimation Equation of Average Atmospheric Temperature The U.S. standard atmosphere 1976 Ta=25.9396+0.88045 Tropical average atmosphere Ta=17.9769+0.91715 Mid-latitude summer average atmosphere Ta=16.0110+0.92621 Mid-latitude winter average atmosphere Ta=19.2704+0.91118

The mean atmospheric temperature of four standard atmospheres can be calculated from the formulas show in Table 1, is Near-surface temperature. , ↑ and ↓ can be calculated from the official website of National Aeronautics and Space Administration (NASA) (https://atmcorr.gsfc.nasa.gov/) for input related parameters. 682

Result and Discussion Using RTE and TIRS_SC to retrieve LST of the main urban area of Kunming based on the Landsat 8 image on April 23, 2013. Then use the high precision method to retrieval LST of the main urban area of Kunming in 2013-2015, based on the retrieval of LST to analyze the spatial and temporal distribution of LST. Comparison of RTE and TIRS_SC According to the time of the image acquisition in 2013 and refer to the meteorological data, the temperature of the main urban area of Kunming is 21℃, the relative humidity (RH) is about 20% and the atmospheric pressure is 1020mb. Input the parameters in the NASA official website, the -2 -1 -2 -1 results showed that =0.94, ↑=0.45 W∙m ∙sr ∙μm, ↓=0.79 W∙m ∙sr ∙μm. Based on the equation of the Mid-latitude summer average atmosphere in Table 1, =288.45567K. The LST is retrieved according to Eq. 3 and Eq. 4 respectively. The results are shown in Figure 2 and Table 2 is the statistics of RTE and TIRS_SC.

Figure 2. Retrieved LST from RTE and TIRS10_SC and the brightness temperature. It is clear from Table 2 that LST retrieved using RTE and TIRS_SC are higher than the temperature of the satellite transit, and the results have the same trend. It should be noted that the temperature we usually say is the temperature of the air from the observation station while LST refers to the temperature of the ground. In general, the LST is higher than the temperature. Table 2. The statistics of LST retrieved from different methods(℃). Methods Min LST Max LST Mean LST Std Temperature 21 Brightness temperature 14.60 47.92 29.39 5.38 RTE 17.31 53.19 32.45 5.87 TIRS_SC 17.21 53.48 32.53 5.94 *Std is standard deviation The average of LST retrieved by RTE is 32.45℃, it is higher than the brightness temperature of 3.06℃. And the average of LST retrieved by TIRS_SC is 32.53℃, it is higher than the brightness temperature of 3.14℃, the results of RTE are more accurate. The error in the experiment is inevitable, due to the atmospheric profile and LSE have no access to the measured values, they can only be estimated. The error will be accumulated in this process. Above all, the results of RTE have minimum error, so we use RTE to retrieve LST of the main urban area of Kunming in 2013-2015. Retrieval LST of the Main Urban Area of Kunming in 2013-2015 with RTE

Before calculating the LST based on Eq. 3 we need to estimate τ, L↑ and L↓. The meteorological data and τ, L↑, L↓ on the time of the images acquisition are showed in Table 3.

683 Table 3. The meteorological data and other parameters in different image acquisition. L L RS image T(℃) RH(%) P(mb) τ ↑ ↓ (W·m-2·μm-1·sr-1) LC81290432013110LGN01 21 20 1020 0.94 0.45 0.79 LC81290432014113LGN00 25 22 1019 0.91 0.72 1.24 LC81290432015068LGN00 19 24 1025 0.93 0.48 0.82

According to the equation (Eq. 1, Eq. 2 and Eq. 3), and use the data in Table 3 to retrieve the LST for the main urban area of Kunming, as shown in Figure 3. Table 4 is the statistics of the retrieved LST in RTE.

Figure 3. The LST of the main urban area of Kunming retrieved from RTE. Table 4. The statistics of LST retrieved from RTE. Time Min LST Max LST Mean LST Std 2013 17.31 53.19 32.45 5.87 2014 18.20 58.44 32.49 5.45 2015 5.85 44.83 26.66 4.28 *Std is standard deviation

It can be seen from Figure 3 and Table 4 that the minimum LST is in Dian Lake and some other places where there is water owing to the specific heat capacity of water is big, so after the water absorbs solar radiation, the temperature rises slowly. The maximum LST is in the urban area for it is more likely to absorb and store the heat of sunlight than water. Therefore, the LST of the construction land is higher than that of the water. For another is that where there is a vegetation cover the LST is between water and the buildings. Vegetation can effectively reduce the LST. Studies had shown that when the vegetation coverage was less than 25%, the vegetation coverage increased by 10% can reduce the LST of 0.6℃, and when the vegetation coverage was 25%-50%, the corresponding cooling down to 0.3℃ [15].

Conclusion As the atmospheric profile and LSE are estimates, the retrieval of LST has errors. The retrieval results showed that the RTE has high accuracy than the TIRS_SC owing to the TIRS_SC is sensitive to atmospheric water vapor content and LSE. The LST of vegetation coverage area is low because vegetation has a cooling effect on the surface. The LST of most areas of and Guandu District are high and the low LST are distributed in the Dian Lake and other places where there is water.

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Acknowledgement This research was financially supported by the National Natural Science Foundation of China (41501436).

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