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Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data

Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data Hindawi Publishing Corporation Journal of Sensors Volume 2016, Article ID 1480307, 8 pages http://dx.doi.org/10.1155/2016/1480307 Research Article Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data Ugur Avdan and Gordana Jovanovska Research Institute of Earth and Space Sciences, Anadolu University, Iki Eylul Campus, 26555 Eskisehir, Turkey Correspondence should be addressed to Ugur Avdan; uavdan@anadolu.edu.tr Received 25 November 2015; Revised 21 January 2016; Accepted 4 February 2016 Academic Editor: Guiyun Tian Copyright © 2016 U. Avdan and G. Jovanovska. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Land surface temperature is an important factor in many areas, such as global climate change, hydrological, geo-/biophysical, and urban land use/land cover. As the latest launched satellite from the LANDSAT family, LANDSAT 8 has opened new possibilities for understanding the events on the Earth with remote sensing. This study presents an algorithm for the automatic mapping of land surface temperature from LANDSAT 8 data. The tool was developed using the LANDSAT 8 thermal infrared sensor Band 10 data. Different methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us study the thermal environment of the ground surface. To verify the algorithm, the land surface temperature and the near-air temperature ∘ ∘ were compared. eTh results showed that, for the first case, the standard deviation was 2.4 C, and for the second case, it was 2.7 C. For future studies, the tool should be refined with in situ measurements of land surface temperature. 1. Introduction for doing pixel calculations. Without the tool, the process of retrieving LST is very long, and it is prone to many Land surface temperature (LST) is defined as the temperature mistakes.Thetoolalsocan be developedinany software felt when thelandsurface is touchedwiththe handsor supporting pixel calculations from a given image, following the skin temperature of the ground [1]. As one of the most step by step this paper. Although an LST retrieval method for important aspects of the land surface, LST has been a main LANDSAT 8 has been developed [1, 4], a tool is needed for topic for developing methodologies to be measured from the complicated process of obtaining the LST. A similar study space. LST is an important factor in many areas of studies, for retrieving LST in ERDAS IMAGINE has been conducted such as global climate change, hydrological and agricultural forLANDSAT 7data[5] butnot forLANDSAT 8. eTh tool processes, and urban land use/land cover. Calculating LST presented in this paper is used for calculating the LST of from remote sensed images is needed since it is an important a given LANDSAT 8 image with the input of the fourth factor controlling most physical, chemical, and biological (red wavelength/micrometres, 0.64–0.67), fifth (near infrared processes of the Earth [2]. er Th e is a growing awareness (NIR) wavelength/micrometres, 0.85–0.88), and tenth (ther- among environmental scientists that remote sensing can and mal infrared sensor (TIRS) wavelength/micrometres, 10.60– must play a role in providing the data needed to assess 11.19) bands. Following January 6, 2014, recommendations of ecosystems conditions and to monitor change at all special USGS of not using TIRS Band 11 due to its larger calibration scales [3]. The tool developed in this paper is simple and does uncertainty, only Band 10 was included in the algorithm. not require any background knowledge so scientists can use it very easy in their researches. 2. Data and Methods eTh algorithmintroducedinthispaper hasbeendevel- oped using ERDAS IMAGINE 2014, with the Model Maker The algorithm was created in ERDAS IMAGINE 2014, and allowing us to create a model that will repeat the process it canonlybeusedtoprocess LANDSAT8data because automatically, anditiseasytodevelopasimpletooluseful of the data complexity. eTh LST of any Landsat 8 satellite 2 Journal of Sensors Input Band 10 Input Band 4 Input Band 5 Top of atmospheric Calculating NDVI spectral radiance (see Section 2.3.1) (see Section 2.1) Calculating proportion of vegetation P (see Section 2.3.2) Conversions of Determination of radians to at-sensor ground emissivity temperature (see Section 2.3.3) (see Section 2.2) Calculating LST (see (7)) LST result Figure 1: Flowchart for LST retrieval. Table 1: Metadata of the satellite images. image can be retrieved following the steps of Figure 1. The data of Landsat 8 is available at the Earth Explorer website Thermal constant, Band 10 free of charge. In this study, the TIR band 10 was used to 𝐾 1321.08 estimate brightness temperature and bands 4 and 5 were used 1 for calculating the NDVI. eTh metadata of the satellite images 𝐾 777.89 used in the algorithm is presented in Table 1. Rescaling factor, Band 10 𝑀 0.000342 2.1. Top of Atmospheric Spectral Radiance. The rfi st step of the 𝐴 0.1 algorithm is the input of Band 10. After inputting band 10, in Correction, Band 10 the background, the tool uses formulas taken from the USGS 𝑂 0.29 webpageforretrievingthetopofatmospheric(TOA)spectral radiance ( ): = 𝑀 ∗𝑄 +𝐴 −𝑂 , (1) 𝐿 cal 𝐿 𝑖 used in the tool’s algorithm to convert reflectance to BT [7]: where 𝑀 represents the band-specific multiplicative rescal- ing factor, 𝑄 is the Band 10 image, 𝐴 is the band-specific cal 𝐿 additive rescaling factor, and 𝑂 is the correction for Band 10 𝑖 BT = − 273.15, (2) ln[(𝐾 /𝐿𝜆) + 1] [6]. 2.2. Conversion of Radiance to At-Sensor Temperature. After where𝐾 and𝐾 stand for the band-specicfi thermal conver- 1 2 thedigital numbers(DNs) areconverted to reflection, the sion constants from the metadata. TIRS band data should be converted from spectral radiance For obtaining the results in Celsius, the radiant tem- to brightness temperature (BT) using the thermal constants perature is revisedbyadding theabsolutezero(approx. provided in the metadata file. eTh following equation is −273.15 C) [8]. 𝐿𝜆 𝐿𝜆 Journal of Sensors 3 2.3. NDVI Method for Emissivity Correction When the NDVI is less than 0, it is classified as water, and the emissivity value of 0.991 is assigned. For NDVI values 2.3.1. Calculating NDVI. Landsat visible and near-infrared between0and0.2,itisconsidered that thelandiscovered bandswereusedfor calculatingthe Normal Dieff rence with soil, and the emissivity value of 0.996 is assigned. Values Vegetation Index (NDVI). eTh importance of estimating the between 0.2 and 0.5 are considered mixtures of soil and NDVI is essential since the amount of vegetation present is vegetation cover and (6) is applied to retrieve the emissivity. an important factor and NDVI can be used to infer general In the last case, when the NDVI value is greater than 0.5, it vegetation condition [9]. eTh calculation of the NDVI is is considered to be covered with vegetation, and the value of important because, afterward, the proportion of the vegeta- 0.973 is assigned. tion (𝑃 )shouldbecalculated,andtheyarehighlyrelatedwith The last step of retrieving the LST or the emissivity- theNDVI, andemissivity(𝜀 ) should be calculated, which is correctedlandsurface temperature𝑇 is computed as follows related to the 𝑃 : [14]: NIR(band 5)−𝑅 (band 4) BT NDVI = , (3) 𝑇 = , (7) NIR(band 5)+𝑅 (band 4) {1 + [(𝜆 BT/𝜌) ln𝜀 ]} where NIR represents the near-infrared band (Band 5) and 𝑅 where 𝑇 is the LST in Celsius ( C, (2)), BT is at-sensor BT represents the red band (Band 4). 𝑠 ( C), 𝜆 is the wavelength of emitted radiance (for which the peak response and the average of the limiting wavelength (𝜆= 2.3.2. Calculating the Proportion of Vegetation. 𝑃 is cal- 10.895)[15]willbeused),𝜀 istheemissivitycalculatedin(6), culated according to (4). A method for calculating 𝑃 [4] and suggests using the NDVI values for vegetation and soil (NDVI = 0.5 and NDVI = 0.2) to apply in global conditions V 𝑠 −2 𝜌=ℎ = 1.438 × 10 mK, (8) [10]: −23 NDVI − NDVI where 𝜎 is the Boltzmann constant (1.38 × 10 J/K), ℎ is 𝑃 =( ) . (4) −34 NDVI − NDVI Planck’s constant (6.626 × 10 J s), and 𝑐 is the velocity of V 𝑠 light (2.998 × 10 m/s) [9]. However, since the NDVI values differ for every area, the valueforvegetatedsurfaces,0.5,maybetoolow.Globalvalues 3. LST Validation from NDVI canbecalculatedfromat-surfacereflectivities, butitwould notbepossibletoestablish global values in eTh two major LST validation models are through ground thecaseofanNDVIcomputedfromTOA reflectivities, measurements or near-surface air temperature [16, 17]. The since NDVI and NDVI will depend on the atmospheric V 𝑠 LST results comparing with the ground measurements results conditions [11]. mayhaveanerror up to 5 C; in thecaseofSrivastavaetal.,the accuracy of the results in some area showed difference of ±2 C 2.3.3. Calculating Land Surface Emissivity. The land surface with actual ground temperature measurements. According to emissivity (LSE (𝜀 )) must be known in order to estimate LST, Liu and Zhang, another method using the mean near-surface since the LSE is a proportionality factor that scales blackbody air temperature to verify the retrieved LST results showed that radiance (Planck’s law) to predict emitted radiance, and it the LST retrieving error is about 0.7 C. For the validation, six is the efficiency of transmitting thermal energy across the representative points have been used. surface into the atmosphere [12]. eTh determination of the For the validation of the n fi al retrieved LST results in the ground emissivity is calculated conditionally as suggested in presented tool, the mean near-surface air temperature was [10]: used [18] but with bigger amount of data and taking not only themeantemperature butalsothe actual temperatureinthe 𝜀 =𝜀 𝑃 +𝜀 (1−𝑃 )+𝐶 , (5) 𝜆 V𝜆 V V 𝜆 given pixel at the moment of the satellite passing over the area for 27 representative points. where 𝜀 and 𝜀 are the vegetation and soil emissivities, V 𝑠 The comparison was made with air temperature, which respectively, and 𝐶 represents the surface roughness (𝐶 =0 is different and can sometimes result in big differences for homogenous and flat surfaces) taken as a constant value since the resolution of LANDSAT 8 for the used bands of 0.005 [13]. The condition can be represented with the is 100 m for the thermal band and 30 m for the red and following formula and the emissivity constant values shown NIR bands. The LST was calculated and taken for the in Table 1 [4]: pixel in which the meteorological station fell. Sometimes, 𝜆 the differences can be very big depending on the weather condition and other factors [19]. It should also be taken 𝜀 , NDVI < NDVI , { 𝑠 into consideration that there is 1.1 to 2 meters’ differ- (6) ence between the LST and the air temperature, which 𝜀 𝑃 +𝜀 (1 − 𝑃 )+𝐶, NDVI ≤ NDVI ≤ NDVI , V𝜆 V V 𝑠 V means that differences in the temperatures are normal and 𝜀 +𝐶, NDVI > NDVI . { expected. 𝑠𝜆 𝑠𝜆 𝑠𝜆 𝑠𝜆 4 Journal of Sensors (b) (a) W E (c) (d) Figure 2: Application of algorithm in Ontario and Quebec, Canada. (a) Geographic location of Ontario in Canada; (b) frames of satellite images of study areas; (c) first case located between Toronto and Huntsville; (d) second case located in surrounding area of the city of Moncton. Table 2: Emissivity of representative terrestrial materials for LAND- 3.1. Application of the Algorithm to Ontario and Quebec, SAT 8 TIRS Band 10. Canada. Hourly data were collected from the Canadian Weather and Meteorology website (http://climate.weather.gc Terrestrial material Water Building Soil Vegetation .ca/) and used for comparison with the retrieved LST for Emissivity 0.991 0.962 0.966 0.973 which, according to the available data, satellite images were downloaded for 02/05/2015 (Toronto area) and 04/06/2015 (Moncton area) for the areas shown in Figure 2. eTh study area was the Canadian provinces of Ontario 4. Conclusion and Quebec (Figure 2). One satellite image was downloaded from each of the two provinces. eTh se areas were chosen This paper presented a new LST software tool and its because of their specifications. That is, both study areas algorithm created in ERDAS for calculating the LST from included water, urban areas, and green areas. LANDSAT 8 TIRS. eTh algorithm was derived using the observed thermal radiance of the TIRS Band 10 of LANDSAT 3.2. Comparison of LST Validation Results. To compare the 8 TIRS. To verify the final retrieved LST results, the near- results, two different satellite images from two different surface air temperature method was used. From the anal- dates in two different areas were chosen according to the ysis of the two areas in Canada from two different dates, available data. After downloading the satellite images from the standard deviation calculated for the rfi st case based http://earthexplorer.usgs.gov/, LSTs were retrieved in ERDAS on 16 meteorological stations was 2.4 C, and that for the using the algorithm presented in this paper. In the rfi st case, second case based on 11 stations was 2.7 C. It should be the satellite image was located between Toronto and the city mentioned that sometimes, the difference between the near- of Huntsville near Lake Simcoe in Ontario, Canada. For this surface temperature and the LST can be drastic since we area, 20 meteorological stations were found, but only 16 of are comparing two different temperatures in different places them were used for the accuracy assessment because of the (ground temperature and 1.1 to 2.0 m off the ground). It presence of clouds or other unwanted events. The differences should also be takenintoconsiderationthatthe resolution between the retrieved LSTs and the air temperatures and of the LANDSAT 8 TIRS data is 100 m for the thermal band details on the stations are presented in Table 2 and Figure 3. and 30 m for the red and NIR bands. Values smaller than In thesecondcase, thestudy area waslocated in the −5 C in the two cases were considered to be clouds or other area surrounding the city of Moncton and included part of unwanted events on the satellite images since the data were New Brunswick, Prince Edward Island, and Nova Scotia in from springtime; it was not expected. From Tables 3 and 4 Canada. For this area, we found 11 meteorological stations, and Figures 3 and 4, it can be concluded that, for the rfi st and all of them were used for the accuracy assessment. eTh case, the smallest difference between the LST retrieved from details are presented in Table 3 and Figure 4. thepresentedtoolandthenear-airtemperaturewas0.7 Cand Journal of Sensors 5 W S E Collingwood Pa Hardwood Mountain Bike Park Lagoon City Barrie Oro Borden Awos Egbert CS Pa Tornto Pa Udora Strong Inter. Mono Centre Uxbridge West Pa Caledon E Park Pa Uxbridge Taris Pa Atmos Erin Pa Atmos Vaughan Pa Claremont Silo Farm Pa Atmos Vaughan Pa Atmos Claremont Pa Atmos Bran. Toronto Button Ville A Pa Markham North Toyota 0 5 10 20 (km) Meteorological stations LST ( C) 10–17 C <−5 C ∘ ∘ 17–20 C −5–3 C ∘ ∘ 20–24 C 3–6 C 6–9 C 24-25 C >−25 C 9-10 C Figure 3: Retrieved LST image and meteorological stations from first study area used in accuracy assessment. 6 Journal of Sensors W E Doaktown Auto RCS Buctouche CDA Cs Pa Atmos Erin Moncton Intl A Gagetown A Gagetown Awos Napan Auto Mechanic Settlement Fundy Park (Alma) Cs Parsboro Saint Jhon A 0 5 10 20 (km) Meteorological stations LST ( C) ∘ ∘ <−5 C 10-11 C −5–3 C 11–13 C 3–6 C 13–17 C 6–9 C 17–20 C ∘ ∘ 9-10 C >−20 C Figure 4: Retrieved LST image and meteorological stations from second study area used in accuracy assessment. Journal of Sensors 7 Table 3: Details and differences of station from first study case. ST name Data 12pm LST Difference Latitude Longitude 𝐻 ∘ 󸀠 ∘ 󸀠 Barrie-Oro 19.9 20.9 −1.0 44 29 00.000 79 33 00.000 289.00 m ∘ 󸀠 ∘ 󸀠 Pa Hardwood Mountain Bike Park 19.2 24.9 −5.7 44 31 08.900 79 35 24.200 334.50 m ∘ 󸀠 ∘ 󸀠 Borden Awos 20.0 20.7 −0.7 44 16 20.000 79 54 42.000 222.50 m ∘ 󸀠 ∘ 󸀠 Pa Udora Strong 19.8 22.3 −2.5 44 15 03.600 79 12 18.300 266.50 m ∘ 󸀠 ∘ 󸀠 Lagoon City 10.7 9.2 1.5 44 32 50.000 79 13 00.000 220.70 m ∘ 󸀠 ∘ 󸀠 Collingwood 13.8 18.7 −4.9 44 30 00.000 80 13 00.000 179.80 m ∘ 󸀠 ∘ 󸀠 Mono Centre 19.1 18.4 0.7 44 01 56.100 80 01 28.010 436.00 m ∘ 󸀠 ∘ 󸀠 Uxbridge West 19.4 22.3 −2.9 44 05 54.000 79 09 49.020 325.00 m ∘ 󸀠 ∘ 󸀠 Pa Uxbridge Taris 19.7 23.2 −3.5 44 03 16.000 79 06 55.000 359.50 m ∘ 󸀠 ∘ 󸀠 Pa Atmos Vaughan 20.9 17.9 3.1 43 51 47.700 79 32 28.900 254.00 m ∘ 󸀠 ∘ 󸀠 Pa Angus Glen Golf Club 20.4 23.3 −2.9 43 54 29.800 79 19 23.400 230.50 m ∘ 󸀠 ∘ 󸀠 Toronto Buttonville A 21.2 23.3 −2.1 43 51 44.000 79 22 12.000 198.10 m ∘ 󸀠 ∘ 󸀠 Pa Claremont Silo Farm 19.8 21.8 −2.0 43 59 37.900 79 05 43.900 263.50 m ∘ 󸀠 ∘ 󸀠 Pa Markham North Toyota 21.6 27.4 −5.8 43 49 01.000 79 20 33.810 187.50 m ∘ 󸀠 ∘ 󸀠 Pa Atmos Claremont 21.5 23.5 −2.0 43 56 09.800 79 05 05.400 167.00 m ∘ 󸀠 ∘ 󸀠 Pa Atmos Erin 19.8 21.6 −1.8 43 49 33.620 80 07 12.840 470.00 m Table 4: Details and differences of station from second study case. ST name Data 11pm LST Difference Latitude Longitude 𝐻 ∘ 󸀠 ∘ 󸀠 Mechanic Settlement 8.4 8.6 −0.2 45 41 37.040 65 09 54.040 403.00 m ∘ 󸀠 ∘ 󸀠 Moncton Intl A 15.3 14.3 1.0 46 06 44.000 64 40 43.000 70.70 m ∘ 󸀠 ∘ 󸀠 Fundy Park (Alma) Cs 12.4 10.2 2.2 45 36 00.000 64 57 00.000 42.70 m ∘ 󸀠 ∘ 󸀠 Buctouche Cda Cs 15.1 14.9 0.2 46 25 49.006 64 46 05.009 35.90 m ∘ 󸀠 ∘ 󸀠 Nappan Auto 15.4 13.3 2.1 45 45 34.400 64 14 29.200 19.80 m ∘ 󸀠 ∘ 󸀠 Doaktown Auto Rcs 15.9 11.5 4.4 46 35 06.090 66 00 35.071 43.00 m ∘ 󸀠 ∘ 󸀠 Saint John A 15.1 12.5 2.6 45 18 58.000 65 53 24.000 108.80 m ∘ 󸀠 ∘ 󸀠 Parrsboro 15.9 8.1 7.8 45 24 48.000 64 20 49.000 30.90 m ∘ 󸀠 ∘ 󸀠 Gagetown A 15.0 10.2 4.8 45 50 00.000 66 26 00.000 50.60 m ∘ 󸀠 ∘ 󸀠 Gagetown Awos A 16.3 18.6 −2.3 45 50 20.000 66 26 59.000 / ∘ 󸀠 ∘ 󸀠 Summerside 13.9 11.7 2.2 46 26 28.000 63 50 17.000 12.20 m the biggest was 5.8 C. In the second study case, the smallest [2] F. Becker and Z.-L. 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Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data

Journal of SensorsJan 1, 2016

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10.1155/2016/1480307
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Hindawi Publishing Corporation Journal of Sensors Volume 2016, Article ID 1480307, 8 pages http://dx.doi.org/10.1155/2016/1480307 Research Article Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data Ugur Avdan and Gordana Jovanovska Research Institute of Earth and Space Sciences, Anadolu University, Iki Eylul Campus, 26555 Eskisehir, Turkey Correspondence should be addressed to Ugur Avdan; uavdan@anadolu.edu.tr Received 25 November 2015; Revised 21 January 2016; Accepted 4 February 2016 Academic Editor: Guiyun Tian Copyright © 2016 U. Avdan and G. Jovanovska. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Land surface temperature is an important factor in many areas, such as global climate change, hydrological, geo-/biophysical, and urban land use/land cover. As the latest launched satellite from the LANDSAT family, LANDSAT 8 has opened new possibilities for understanding the events on the Earth with remote sensing. This study presents an algorithm for the automatic mapping of land surface temperature from LANDSAT 8 data. The tool was developed using the LANDSAT 8 thermal infrared sensor Band 10 data. Different methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us study the thermal environment of the ground surface. To verify the algorithm, the land surface temperature and the near-air temperature ∘ ∘ were compared. eTh results showed that, for the first case, the standard deviation was 2.4 C, and for the second case, it was 2.7 C. For future studies, the tool should be refined with in situ measurements of land surface temperature. 1. Introduction for doing pixel calculations. Without the tool, the process of retrieving LST is very long, and it is prone to many Land surface temperature (LST) is defined as the temperature mistakes.Thetoolalsocan be developedinany software felt when thelandsurface is touchedwiththe handsor supporting pixel calculations from a given image, following the skin temperature of the ground [1]. As one of the most step by step this paper. Although an LST retrieval method for important aspects of the land surface, LST has been a main LANDSAT 8 has been developed [1, 4], a tool is needed for topic for developing methodologies to be measured from the complicated process of obtaining the LST. A similar study space. LST is an important factor in many areas of studies, for retrieving LST in ERDAS IMAGINE has been conducted such as global climate change, hydrological and agricultural forLANDSAT 7data[5] butnot forLANDSAT 8. eTh tool processes, and urban land use/land cover. Calculating LST presented in this paper is used for calculating the LST of from remote sensed images is needed since it is an important a given LANDSAT 8 image with the input of the fourth factor controlling most physical, chemical, and biological (red wavelength/micrometres, 0.64–0.67), fifth (near infrared processes of the Earth [2]. er Th e is a growing awareness (NIR) wavelength/micrometres, 0.85–0.88), and tenth (ther- among environmental scientists that remote sensing can and mal infrared sensor (TIRS) wavelength/micrometres, 10.60– must play a role in providing the data needed to assess 11.19) bands. Following January 6, 2014, recommendations of ecosystems conditions and to monitor change at all special USGS of not using TIRS Band 11 due to its larger calibration scales [3]. The tool developed in this paper is simple and does uncertainty, only Band 10 was included in the algorithm. not require any background knowledge so scientists can use it very easy in their researches. 2. Data and Methods eTh algorithmintroducedinthispaper hasbeendevel- oped using ERDAS IMAGINE 2014, with the Model Maker The algorithm was created in ERDAS IMAGINE 2014, and allowing us to create a model that will repeat the process it canonlybeusedtoprocess LANDSAT8data because automatically, anditiseasytodevelopasimpletooluseful of the data complexity. eTh LST of any Landsat 8 satellite 2 Journal of Sensors Input Band 10 Input Band 4 Input Band 5 Top of atmospheric Calculating NDVI spectral radiance (see Section 2.3.1) (see Section 2.1) Calculating proportion of vegetation P (see Section 2.3.2) Conversions of Determination of radians to at-sensor ground emissivity temperature (see Section 2.3.3) (see Section 2.2) Calculating LST (see (7)) LST result Figure 1: Flowchart for LST retrieval. Table 1: Metadata of the satellite images. image can be retrieved following the steps of Figure 1. The data of Landsat 8 is available at the Earth Explorer website Thermal constant, Band 10 free of charge. In this study, the TIR band 10 was used to 𝐾 1321.08 estimate brightness temperature and bands 4 and 5 were used 1 for calculating the NDVI. eTh metadata of the satellite images 𝐾 777.89 used in the algorithm is presented in Table 1. Rescaling factor, Band 10 𝑀 0.000342 2.1. Top of Atmospheric Spectral Radiance. The rfi st step of the 𝐴 0.1 algorithm is the input of Band 10. After inputting band 10, in Correction, Band 10 the background, the tool uses formulas taken from the USGS 𝑂 0.29 webpageforretrievingthetopofatmospheric(TOA)spectral radiance ( ): = 𝑀 ∗𝑄 +𝐴 −𝑂 , (1) 𝐿 cal 𝐿 𝑖 used in the tool’s algorithm to convert reflectance to BT [7]: where 𝑀 represents the band-specific multiplicative rescal- ing factor, 𝑄 is the Band 10 image, 𝐴 is the band-specific cal 𝐿 additive rescaling factor, and 𝑂 is the correction for Band 10 𝑖 BT = − 273.15, (2) ln[(𝐾 /𝐿𝜆) + 1] [6]. 2.2. Conversion of Radiance to At-Sensor Temperature. After where𝐾 and𝐾 stand for the band-specicfi thermal conver- 1 2 thedigital numbers(DNs) areconverted to reflection, the sion constants from the metadata. TIRS band data should be converted from spectral radiance For obtaining the results in Celsius, the radiant tem- to brightness temperature (BT) using the thermal constants perature is revisedbyadding theabsolutezero(approx. provided in the metadata file. eTh following equation is −273.15 C) [8]. 𝐿𝜆 𝐿𝜆 Journal of Sensors 3 2.3. NDVI Method for Emissivity Correction When the NDVI is less than 0, it is classified as water, and the emissivity value of 0.991 is assigned. For NDVI values 2.3.1. Calculating NDVI. Landsat visible and near-infrared between0and0.2,itisconsidered that thelandiscovered bandswereusedfor calculatingthe Normal Dieff rence with soil, and the emissivity value of 0.996 is assigned. Values Vegetation Index (NDVI). eTh importance of estimating the between 0.2 and 0.5 are considered mixtures of soil and NDVI is essential since the amount of vegetation present is vegetation cover and (6) is applied to retrieve the emissivity. an important factor and NDVI can be used to infer general In the last case, when the NDVI value is greater than 0.5, it vegetation condition [9]. eTh calculation of the NDVI is is considered to be covered with vegetation, and the value of important because, afterward, the proportion of the vegeta- 0.973 is assigned. tion (𝑃 )shouldbecalculated,andtheyarehighlyrelatedwith The last step of retrieving the LST or the emissivity- theNDVI, andemissivity(𝜀 ) should be calculated, which is correctedlandsurface temperature𝑇 is computed as follows related to the 𝑃 : [14]: NIR(band 5)−𝑅 (band 4) BT NDVI = , (3) 𝑇 = , (7) NIR(band 5)+𝑅 (band 4) {1 + [(𝜆 BT/𝜌) ln𝜀 ]} where NIR represents the near-infrared band (Band 5) and 𝑅 where 𝑇 is the LST in Celsius ( C, (2)), BT is at-sensor BT represents the red band (Band 4). 𝑠 ( C), 𝜆 is the wavelength of emitted radiance (for which the peak response and the average of the limiting wavelength (𝜆= 2.3.2. Calculating the Proportion of Vegetation. 𝑃 is cal- 10.895)[15]willbeused),𝜀 istheemissivitycalculatedin(6), culated according to (4). A method for calculating 𝑃 [4] and suggests using the NDVI values for vegetation and soil (NDVI = 0.5 and NDVI = 0.2) to apply in global conditions V 𝑠 −2 𝜌=ℎ = 1.438 × 10 mK, (8) [10]: −23 NDVI − NDVI where 𝜎 is the Boltzmann constant (1.38 × 10 J/K), ℎ is 𝑃 =( ) . (4) −34 NDVI − NDVI Planck’s constant (6.626 × 10 J s), and 𝑐 is the velocity of V 𝑠 light (2.998 × 10 m/s) [9]. However, since the NDVI values differ for every area, the valueforvegetatedsurfaces,0.5,maybetoolow.Globalvalues 3. LST Validation from NDVI canbecalculatedfromat-surfacereflectivities, butitwould notbepossibletoestablish global values in eTh two major LST validation models are through ground thecaseofanNDVIcomputedfromTOA reflectivities, measurements or near-surface air temperature [16, 17]. The since NDVI and NDVI will depend on the atmospheric V 𝑠 LST results comparing with the ground measurements results conditions [11]. mayhaveanerror up to 5 C; in thecaseofSrivastavaetal.,the accuracy of the results in some area showed difference of ±2 C 2.3.3. Calculating Land Surface Emissivity. The land surface with actual ground temperature measurements. According to emissivity (LSE (𝜀 )) must be known in order to estimate LST, Liu and Zhang, another method using the mean near-surface since the LSE is a proportionality factor that scales blackbody air temperature to verify the retrieved LST results showed that radiance (Planck’s law) to predict emitted radiance, and it the LST retrieving error is about 0.7 C. For the validation, six is the efficiency of transmitting thermal energy across the representative points have been used. surface into the atmosphere [12]. eTh determination of the For the validation of the n fi al retrieved LST results in the ground emissivity is calculated conditionally as suggested in presented tool, the mean near-surface air temperature was [10]: used [18] but with bigger amount of data and taking not only themeantemperature butalsothe actual temperatureinthe 𝜀 =𝜀 𝑃 +𝜀 (1−𝑃 )+𝐶 , (5) 𝜆 V𝜆 V V 𝜆 given pixel at the moment of the satellite passing over the area for 27 representative points. where 𝜀 and 𝜀 are the vegetation and soil emissivities, V 𝑠 The comparison was made with air temperature, which respectively, and 𝐶 represents the surface roughness (𝐶 =0 is different and can sometimes result in big differences for homogenous and flat surfaces) taken as a constant value since the resolution of LANDSAT 8 for the used bands of 0.005 [13]. The condition can be represented with the is 100 m for the thermal band and 30 m for the red and following formula and the emissivity constant values shown NIR bands. The LST was calculated and taken for the in Table 1 [4]: pixel in which the meteorological station fell. Sometimes, 𝜆 the differences can be very big depending on the weather condition and other factors [19]. It should also be taken 𝜀 , NDVI < NDVI , { 𝑠 into consideration that there is 1.1 to 2 meters’ differ- (6) ence between the LST and the air temperature, which 𝜀 𝑃 +𝜀 (1 − 𝑃 )+𝐶, NDVI ≤ NDVI ≤ NDVI , V𝜆 V V 𝑠 V means that differences in the temperatures are normal and 𝜀 +𝐶, NDVI > NDVI . { expected. 𝑠𝜆 𝑠𝜆 𝑠𝜆 𝑠𝜆 4 Journal of Sensors (b) (a) W E (c) (d) Figure 2: Application of algorithm in Ontario and Quebec, Canada. (a) Geographic location of Ontario in Canada; (b) frames of satellite images of study areas; (c) first case located between Toronto and Huntsville; (d) second case located in surrounding area of the city of Moncton. Table 2: Emissivity of representative terrestrial materials for LAND- 3.1. Application of the Algorithm to Ontario and Quebec, SAT 8 TIRS Band 10. Canada. Hourly data were collected from the Canadian Weather and Meteorology website (http://climate.weather.gc Terrestrial material Water Building Soil Vegetation .ca/) and used for comparison with the retrieved LST for Emissivity 0.991 0.962 0.966 0.973 which, according to the available data, satellite images were downloaded for 02/05/2015 (Toronto area) and 04/06/2015 (Moncton area) for the areas shown in Figure 2. eTh study area was the Canadian provinces of Ontario 4. Conclusion and Quebec (Figure 2). One satellite image was downloaded from each of the two provinces. eTh se areas were chosen This paper presented a new LST software tool and its because of their specifications. That is, both study areas algorithm created in ERDAS for calculating the LST from included water, urban areas, and green areas. LANDSAT 8 TIRS. eTh algorithm was derived using the observed thermal radiance of the TIRS Band 10 of LANDSAT 3.2. Comparison of LST Validation Results. To compare the 8 TIRS. To verify the final retrieved LST results, the near- results, two different satellite images from two different surface air temperature method was used. From the anal- dates in two different areas were chosen according to the ysis of the two areas in Canada from two different dates, available data. After downloading the satellite images from the standard deviation calculated for the rfi st case based http://earthexplorer.usgs.gov/, LSTs were retrieved in ERDAS on 16 meteorological stations was 2.4 C, and that for the using the algorithm presented in this paper. In the rfi st case, second case based on 11 stations was 2.7 C. It should be the satellite image was located between Toronto and the city mentioned that sometimes, the difference between the near- of Huntsville near Lake Simcoe in Ontario, Canada. For this surface temperature and the LST can be drastic since we area, 20 meteorological stations were found, but only 16 of are comparing two different temperatures in different places them were used for the accuracy assessment because of the (ground temperature and 1.1 to 2.0 m off the ground). It presence of clouds or other unwanted events. The differences should also be takenintoconsiderationthatthe resolution between the retrieved LSTs and the air temperatures and of the LANDSAT 8 TIRS data is 100 m for the thermal band details on the stations are presented in Table 2 and Figure 3. and 30 m for the red and NIR bands. Values smaller than In thesecondcase, thestudy area waslocated in the −5 C in the two cases were considered to be clouds or other area surrounding the city of Moncton and included part of unwanted events on the satellite images since the data were New Brunswick, Prince Edward Island, and Nova Scotia in from springtime; it was not expected. From Tables 3 and 4 Canada. For this area, we found 11 meteorological stations, and Figures 3 and 4, it can be concluded that, for the rfi st and all of them were used for the accuracy assessment. eTh case, the smallest difference between the LST retrieved from details are presented in Table 3 and Figure 4. thepresentedtoolandthenear-airtemperaturewas0.7 Cand Journal of Sensors 5 W S E Collingwood Pa Hardwood Mountain Bike Park Lagoon City Barrie Oro Borden Awos Egbert CS Pa Tornto Pa Udora Strong Inter. Mono Centre Uxbridge West Pa Caledon E Park Pa Uxbridge Taris Pa Atmos Erin Pa Atmos Vaughan Pa Claremont Silo Farm Pa Atmos Vaughan Pa Atmos Claremont Pa Atmos Bran. Toronto Button Ville A Pa Markham North Toyota 0 5 10 20 (km) Meteorological stations LST ( C) 10–17 C <−5 C ∘ ∘ 17–20 C −5–3 C ∘ ∘ 20–24 C 3–6 C 6–9 C 24-25 C >−25 C 9-10 C Figure 3: Retrieved LST image and meteorological stations from first study area used in accuracy assessment. 6 Journal of Sensors W E Doaktown Auto RCS Buctouche CDA Cs Pa Atmos Erin Moncton Intl A Gagetown A Gagetown Awos Napan Auto Mechanic Settlement Fundy Park (Alma) Cs Parsboro Saint Jhon A 0 5 10 20 (km) Meteorological stations LST ( C) ∘ ∘ <−5 C 10-11 C −5–3 C 11–13 C 3–6 C 13–17 C 6–9 C 17–20 C ∘ ∘ 9-10 C >−20 C Figure 4: Retrieved LST image and meteorological stations from second study area used in accuracy assessment. Journal of Sensors 7 Table 3: Details and differences of station from first study case. ST name Data 12pm LST Difference Latitude Longitude 𝐻 ∘ 󸀠 ∘ 󸀠 Barrie-Oro 19.9 20.9 −1.0 44 29 00.000 79 33 00.000 289.00 m ∘ 󸀠 ∘ 󸀠 Pa Hardwood Mountain Bike Park 19.2 24.9 −5.7 44 31 08.900 79 35 24.200 334.50 m ∘ 󸀠 ∘ 󸀠 Borden Awos 20.0 20.7 −0.7 44 16 20.000 79 54 42.000 222.50 m ∘ 󸀠 ∘ 󸀠 Pa Udora Strong 19.8 22.3 −2.5 44 15 03.600 79 12 18.300 266.50 m ∘ 󸀠 ∘ 󸀠 Lagoon City 10.7 9.2 1.5 44 32 50.000 79 13 00.000 220.70 m ∘ 󸀠 ∘ 󸀠 Collingwood 13.8 18.7 −4.9 44 30 00.000 80 13 00.000 179.80 m ∘ 󸀠 ∘ 󸀠 Mono Centre 19.1 18.4 0.7 44 01 56.100 80 01 28.010 436.00 m ∘ 󸀠 ∘ 󸀠 Uxbridge West 19.4 22.3 −2.9 44 05 54.000 79 09 49.020 325.00 m ∘ 󸀠 ∘ 󸀠 Pa Uxbridge Taris 19.7 23.2 −3.5 44 03 16.000 79 06 55.000 359.50 m ∘ 󸀠 ∘ 󸀠 Pa Atmos Vaughan 20.9 17.9 3.1 43 51 47.700 79 32 28.900 254.00 m ∘ 󸀠 ∘ 󸀠 Pa Angus Glen Golf Club 20.4 23.3 −2.9 43 54 29.800 79 19 23.400 230.50 m ∘ 󸀠 ∘ 󸀠 Toronto Buttonville A 21.2 23.3 −2.1 43 51 44.000 79 22 12.000 198.10 m ∘ 󸀠 ∘ 󸀠 Pa Claremont Silo Farm 19.8 21.8 −2.0 43 59 37.900 79 05 43.900 263.50 m ∘ 󸀠 ∘ 󸀠 Pa Markham North Toyota 21.6 27.4 −5.8 43 49 01.000 79 20 33.810 187.50 m ∘ 󸀠 ∘ 󸀠 Pa Atmos Claremont 21.5 23.5 −2.0 43 56 09.800 79 05 05.400 167.00 m ∘ 󸀠 ∘ 󸀠 Pa Atmos Erin 19.8 21.6 −1.8 43 49 33.620 80 07 12.840 470.00 m Table 4: Details and differences of station from second study case. ST name Data 11pm LST Difference Latitude Longitude 𝐻 ∘ 󸀠 ∘ 󸀠 Mechanic Settlement 8.4 8.6 −0.2 45 41 37.040 65 09 54.040 403.00 m ∘ 󸀠 ∘ 󸀠 Moncton Intl A 15.3 14.3 1.0 46 06 44.000 64 40 43.000 70.70 m ∘ 󸀠 ∘ 󸀠 Fundy Park (Alma) Cs 12.4 10.2 2.2 45 36 00.000 64 57 00.000 42.70 m ∘ 󸀠 ∘ 󸀠 Buctouche Cda Cs 15.1 14.9 0.2 46 25 49.006 64 46 05.009 35.90 m ∘ 󸀠 ∘ 󸀠 Nappan Auto 15.4 13.3 2.1 45 45 34.400 64 14 29.200 19.80 m ∘ 󸀠 ∘ 󸀠 Doaktown Auto Rcs 15.9 11.5 4.4 46 35 06.090 66 00 35.071 43.00 m ∘ 󸀠 ∘ 󸀠 Saint John A 15.1 12.5 2.6 45 18 58.000 65 53 24.000 108.80 m ∘ 󸀠 ∘ 󸀠 Parrsboro 15.9 8.1 7.8 45 24 48.000 64 20 49.000 30.90 m ∘ 󸀠 ∘ 󸀠 Gagetown A 15.0 10.2 4.8 45 50 00.000 66 26 00.000 50.60 m ∘ 󸀠 ∘ 󸀠 Gagetown Awos A 16.3 18.6 −2.3 45 50 20.000 66 26 59.000 / ∘ 󸀠 ∘ 󸀠 Summerside 13.9 11.7 2.2 46 26 28.000 63 50 17.000 12.20 m the biggest was 5.8 C. In the second study case, the smallest [2] F. Becker and Z.-L. 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