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A long-term monthly assessment of land surface temperature and normalized difference vegetation index using Landsat data

Uma avaliação mensal de longo prazo da temperatura da superfície da terra e índice de vegetação de diferença normalizada usando dados Landsat

Abstract

The present study assesses the monthly variation of land surface temperature (LST) and the relationship between LST and normalized difference vegetation index (NDVI) in Raipur City of India using one hundred and eighteen Landsat images from 1988 to 2019. The results show that a monthly variation is observed in the mean LST. The highest mean LST is found in April (38.79oC), followed by May (36.64oC), June (34.56oC), and March (32.11oC).The lowest mean LST is observed in January (23.01oC), followed by December (23.76oC), and November (25.83oC). A moderate range of mean LST is noticed in September (27.18oC), October (27.22oC), and February (27.88oC). Pearson's linear correlation method is used to correlate LST with NDVI. The LST-NDVI correlation is strong negative in October (-0.62), September (-0.55), and April (-0.51). The moderate negative correlation is developed in March (-0.40), May (-0.44), June (-0.47), and November (-0.39). A weak negative correlation is observed in December (-0.21), January (-0.24), and February (-0.29). The change in weather elements and variation in land surface characteristics contribute to the monthly fluctuation of mean LST and LST-NDVI correlation. The study will be an effective one for the town and country planners for their future estimation of land conversion.

Keywords:
Landsat; Land surface; Land surface temperature; Normalized difference vegetation index; Raipur

Resumo

O presente estudo avalia a variação mensal da temperatura da superfície terrestre (LST) e a relação entre lST e o índice de vegetação de diferença normalizado (NDVI) na cidade de Raipur, na Índia, utilizando cento e dezoito imagens landsat de 1988 a 2019. Os resultados mostram que uma variação mensal é observada no LST médio. O LST médio mais elevado é encontrado em abril (38,79oC), seguido por maio (36.64oC), junho (34.56oC) e março (32.11oC). O LST médio mais baixo é observado em janeiro (23.01oC), seguido por dezembro (23.76oC) e novembro (25,83oC). Uma gama moderada de LST médio é notada em setembro (27.18oC), outubro (27.22oC) e fevereiro (27.88oC). O método linear de correlação de Pearson é usado para correlacionar LST com NDVI. A correlação LST-NDVI é fortemente negativa em outubro (-0,62), setembro (-0,55) e abril (-0,51). A correlação negativa moderada é desenvolvida em março (-0,40), maio (-0,44), junho (-0,47) e novembro (-0,39). Uma correlação negativa fraca é observada em dezembro (-0,21), janeiro (-0,24) e fevereiro (-0,29). A alteração dos elementos meteorológicos e a variação das características da superfície terrestre contribuem para a flutuação mensal da correlação média LST e LST-NDVI. O estudo será eficaz para os planeadores da cidade e do país para a sua futura estimativa da conversão de terras.

Palavras-chave:
Landsat; Superfície de terra; Temperatura da superfície do terreno; Índice de vegetação de diferença normalizado; O Raipur

Introduction

The thermal infrared (TIR) region of the electromagnetic spectrum has a huge potential in determining the nature and characteristics of land surface dynamics in any natural environment along with the visible and near-infrared (VNIR) and shortwave infrared (SWIR) regions (Chen et al., 2006Chen, XL, Zhao, HM, Li, PX, & Yi, ZY. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens Environ, 104(2), 133-146. https://doi.org/10.1016/j.rse.2005.11.016
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; Ghobadi et al., 2014Ghobadi, Y., Pradhan, B., Shafri, H. Z. M., & Kabiri, K. (2014). Assessment of spatial relationship between land surface temperature and land use/cover retrieval from multi-temporal remote sensing data in South Karkheh Sub-basin, Iran. Arab J Geosci, 8(1), 525-537. https://doi.org/10.1007/s12517-013-1244-3.
https://doi.org/10.1007/s12517-013-1244-...
; Guha et al., 2018Guha, S., Govil, H., Dey, A., & Gill, N. (2018). Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI/TIRS data in Florence and Naples city, Italy. Eur J Remote Sens, 51(1), 667-678. https://doi:10.1080/22797254.2018.1474494.
https://doi:10.1080/22797254.2018.147449...
; Guha & Govil, 2020Govil, H., Guha, S., Diwan, P., Gill, N., & Dey, A. (2020). Analyzing Linear Relationships of LST with NDVI and MNDISI Using Various Resolution Levels of Landsat 8 OLI/TIRS Data. In Sharma N., Chakrabarti A., Balas V. (Eds.), Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing (Vol. 1042, p. 171-184). Singapore: Springer. https://doi.org/10.1007/978-981-32-9949-8_13
https://doi.org/10.1007/978-981-32-9949-...
; Guha & Govil, 2021a; Guha & Govil, 2019; Alexander, 2020Alexander, C. (2020). Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST). Int J Appl Earth Obs Geoinf, 86, 102013. https://doi.org/10.1016/j.jag.2019.102013.
https://doi.org/10.1016/j.jag.2019.10201...
). Land surface temperature (LST) is a major factor to assess the biogeochemical actions in the formation of land surface materials and it is the most essential parameter to evaluate the ecological condition of rural or urban areas (Tomlinson et al., 2011Tomlinson, C. J., Chapman, L., Trones, J. E., & Baker, C. (2011). Remote sensingland surface temperature for meteorology and climatology: a review. Meteorol Appl, 18, 296-306. https://doi.org/10.1002/met.287.
https://doi.org/10.1002/met.287...
; Hao et al., 2016Hao, X., Li, W., & Deng, H. (2016). The oasis effect and summer temperature rise in arid regions-case study in Tarim Basin. Sci Rep, 6, 35418. https://doi.org/10.1038/srep35418.
https://doi.org/10.1038/srep35418...
). LST varies with the changes of tone, texture, pattern, and association of the land surface types in any region (Hou et al., 2010Hou, G. L., Zhang, H. Y., Wang, Y. Q., Qiao, Z. H., & Zhang, Z. X. (2010). Retrieval and Spatial Distribution of Land Surface Temperature in the Middle Part of Jilin Province Based on MODIS Data. SciGeogr Sin, 30, 421-427.). Generally, green vegetation and water bodies present low LST, whereas a built-up area, bare rock surface, or dry soil reflects high LST (Guha et al., 2019). Thus, LST-related studies are very important in urban and land use planning and development (Li et al., 2017Li, W. F., Cao, Q. W., Kun, L., & Wu, J. S. (2017). Linking potential heat source and sink to urban heat island: Heterogeneous effects of landscape pattern on land surface temperature. Sci Total Environ, 586, 457-465. https://doi.org/10.1016/j.scitotenv.2017.01.191.
https://doi.org/10.1016/j.scitotenv.2017...
; Guha, Govil & Besoya, 2020; Guha, Govil, Gill & Dey, 2020a). Urban heat islands and urban hot spots are very common term in an urban environment and are indicated by the zone of very high LST inside the urban bodies (Guha et al., 2017). The most popular spectral index is the normalized difference vegetation index (NDVI) which is used in extracting green vegetation (Yuan et al., 2017Yuan, X., Wang, W., Cui, J., Meng, F., Kurban, A., & De Maeyer, P. (2017). Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia. Sci Rep, 7(1), 3287. https://doi.org/10.1038/s41598017034322.
https://doi.org/10.1038/s41598017034322...
; Guha & Govil, 2021b; Mondal et al., 2011Mondal, A., Guha, S., Mishra, P. K., & Kundu, S. (2011). Land use/Land cover changes in Hugli Estuary using Fuzzy CMean algorithm. Int J Geomat Geosci, 2(2), 613-626.; Guha, 2016; Guha, Govil, Gill & Dey, 2020b; Guha, Govil, Dey et al., 2020; Guha, Govil, Gill & Dey, 2020c; Guha, Govil & Diwan, 2020). NDVI is directly used in the determination of land surface emissivity and thus is a significant factor for LST estimation (Sobrino et al., 2004Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from Landsat TM5. Remote Sens Environ,90(4), 434-440. https://doi.org/10.1016/j.rse.2004.02.003.
https://doi.org/10.1016/j.rse.2004.02.00...
; Carlson & Ripley, 1997Carlson, T. N., & Ripley, D. A. (1997). On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index. Remote Sens Environ, 62(3), 241-252. https://doi.org/10.1016/S0034-4257(97)00104-1.
https://doi.org/10.1016/S0034-4257(97)00...
).

Currently, the relationship between LST and NDVI was constructed using thermal infrared remote sensing technology and only some satellite sensors have the thermal bands like Landsat, MODIS, and ASTER (Wen et al., 2017Wen, L., Peng, W., Yang, H., Wang, H., Dong, L., & Shang, X. (2017). An analysis of land surface temperature (LST) and its influencing factors in summer in western Sichuan Plateau: A case study of Xichang City. Remote Sens Land Res, 29(2), 207-214. https://doi.org/10.6046/gtzyyg.2017.02.30.
https://doi.org/10.6046/gtzyyg.2017.02.3...
; Guha et al., 2017Guha, S., Govil, H., & Mukherjee, S. (2017). Dynamic analysis and ecological evaluation of urban heat islands in Raipur city, India. J Appl Remote Sens, 11(3), 036020. https://doi.org/10.1117/1.JRS.11.036020.
https://doi.org/10.1117/1.JRS.11.036020...
). The required wavelength of these thermal bands is 8-14 µm for LST determination. An infrared thermometer instrument is used to validate the derived LST values (Li et al., 2017Li, W. F., Cao, Q. W., Kun, L., & Wu, J. S. (2017). Linking potential heat source and sink to urban heat island: Heterogeneous effects of landscape pattern on land surface temperature. Sci Total Environ, 586, 457-465. https://doi.org/10.1016/j.scitotenv.2017.01.191.
https://doi.org/10.1016/j.scitotenv.2017...
; Guha, Govil & Besoya, 2020Govil, H., Guha, S., Diwan, P., Gill, N., & Dey, A. (2020). Analyzing Linear Relationships of LST with NDVI and MNDISI Using Various Resolution Levels of Landsat 8 OLI/TIRS Data. In Sharma N., Chakrabarti A., Balas V. (Eds.), Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing (Vol. 1042, p. 171-184). Singapore: Springer. https://doi.org/10.1007/978-981-32-9949-8_13
https://doi.org/10.1007/978-981-32-9949-...
; Guha, Govil, Gill & Dey, 2020a; Guha et al., 2017). LST-NDVI relationship was applied in most of the thermal remote sensing studies that were conducted with temporal discrete data sets on the urban environment, e.g., Tokyo, Melbourne, Shiraz, Raipur (Shigeto, 1994Shigeto, K. (1994). Relation between vegetation, surface temperature, and surface composition in the Tokyo region during winter. Remote Sens Environ, 50(1), 52-60. https://doi.org/10.1016/0034-4257(94)90094-9
https://doi.org/10.1016/0034-4257(94)900...
; Jamei et al., 2019Jamei, Y., Rajagopalan, P., & Sun, Q. C. (2019). Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Sci Total Environ, 659, 1335-1351. https://doi.org/10.1016/j.scitotenv.2018.12.308.
https://doi.org/10.1016/j.scitotenv.2018...
; Fatemi & Narangifard, 2019Fatemi, M., & Narangifard, M. (2019). Monitoring LULC changes and its impact on the LST and NDVI in District 1 of Shiraz City. Arab J Geosci, 12, 127. https://doi.org/10.1007/s12517-019-4259-6.
https://doi.org/10.1007/s12517-019-4259-...
; Guha & Govil, 2021c). Ferelli et al. (2018) correlate LST with NDVI in Monte Hermosoof Argentina. Fewer studies are available on the long-term and continuous seasonal correlation among LST, NDVI, and LULC in a tropical city.

A reverse relationship is built between LST and the concentration of green vegetation and thus, NDVIis used as an important factor for determining LST in most of the LST retrieval methods (Voogt & Oke, 2003Voogt, J. A., & Oke, T. R. (2003). Thermal Remote Sensing of Urban Climates. Remote Sens Environ, 86, 370-384. https://doi.org/10.1016/S0034-4257(03)00079-8.
https://doi.org/10.1016/S0034-4257(03)00...
; Gutman & Ignatov, 1998Gutman, G., & Ignatov, A. (1998). The Derivation of the Green Vegetation Fraction from NOAA/ AVHRR Data for Use in Numerical Weather Prediction Models. Int J Remote Sens, 19(8), 1533-1543. https://doi:10.1080/014311698215333.
https://doi:10.1080/014311698215333...
; Goward et al., 2002Goward, S. N., Xue, Y. K., & Czajkowski, K. P. (2002). Evaluating Land Surface Moisture Conditions from the Remotely Sensed Temperature/Vegetation Index Measurements: An Exploration with the Simplified Simple Biosphere Model. Remote Sens Environ,79, 225-242. https://doi:10.1016/S0034-4257(01)00275-9.
https://doi:10.1016/S0034-4257(01)00275-...
; Govil et al., 2019Govil, H., Guha, S., Dey, A., & Gill, N. (2019). Seasonal evaluation of downscaled land surface temperature: A case study in a humid tropical city. Heliyon, 5(6), e01923. https://doi.org/10.1016/j.heliyon.2019.e01923.
https://doi.org/10.1016/j.heliyon.2019.e...
; Guha, 2021Guha, S. (2021). Dynamic seasonal analysis on LST-NDVI relationship and ecological health of Raipur City, India. Ecosyst Health Sustain. 7(1): 1927852. https://doi.org/10.1080/20964129.2021.1927852.
https://doi.org/10.1080/20964129.2021.19...
; Govil et al., 2020).

There are so many valuable research articles found on LST-NDVI relationships that were conducted mainly in the Chinese landscape (Gui et al., 2019Gui, X., Wang, L., Yao, R., Yu, D., & Li, C. (2019). Investigating the urbanization process and its impact on vegetation change and urban heat island in Wuhan, China. Environ Sci Pollut Res, 26(30), 30808-30825. https://doi.org/10.1007/s11356-019-06273-w.
https://doi.org/10.1007/s11356-019-06273...
; Qu et al., 2020Qu, S., Wang, L., Lin, A., Yu, D, Yuan, M., & Li, C. (2020). Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin, China. Ecol Indic, 108, 105724. https://doi.org/10.1016/j.ecolind.2019.105724.
https://doi.org/10.1016/j.ecolind.2019.1...
; Qu et al., 2018; Cui, Wang, Qu, Singh, Lai, Jiang& Yao, 2019Yao, R., Wang, L., Huang, X., Chen, X., & Liu, Z. (2019). Increased spatial heterogeneity in vegetation greenness due to vegetation greening in mainland China. Ecol Indic, 99, 240-250. https://doi.org/10.1016/j.ecolind.2018.12.039.
https://doi.org/10.1016/j.ecolind.2018.1...
; Cui, Wang, Qu, Singh, Lai& Yao, 2019; Yao, Cao et al., 2019; Yao, Wang, Gui et al., 2017; Yao, Wang, Huang, Chen et al., 2018; Yao, Wang et al., 2019; Yao, Wang, Huang et al., 2017; Yao, Wang, Huang, Zhang et al., 2018; Yuan et al., 2020Yuan, M., Wang, L., Lin, A., Liu, Z., & Qu, S. (2020). Vegetation green up under the influence of daily minimum temperature and urbanization in the Yellow River Basin, China. Ecol Indic, 108, 105760. https://doi.org/10.1016/j.ecolind.2019.105760.
https://doi.org/10.1016/j.ecolind.2019.1...
). Some recent studies successfully analyze the LST-NDVI correlation in some tropical Indian cities (Kikon et al., 2016Kikon, N., Singh, P., Singh, S. K., & Vyas, A. (2016). Assessment of urban heat islands (UHI) of Noida City, India using multi-temporal satellite data. Sustain Cities Soc, 22, 19-28. https://doi.org/10.1016/j.scs.2016.01.005.
https://doi.org/10.1016/j.scs.2016.01.00...
; Kumar & Shekhar, 2015Kumar, D., & Shekhar, S. (2015). Statisticalanalysisofland surface temperature-vegetation indexes relationship through thermal remote sensing. Ecotox Environ Safe, 121, 39-44. https://doi.org/10.1016/j.ecoenv.2015.07.004.
https://doi.org/10.1016/j.ecoenv.2015.07...
; Mathew et al., 2018Mathew, A., Khandelwal, S., & Kaul, N. (2018). Spatio-temporal variations of surface temperatures of Ahmedabad city and its relationship with vegetation and urbanization parameters as indicators of surface temperatures. Remote Sens Appl Soc Environ, 11, 119-139. https://doi.org/10.1016/j.rsase.2018.05.003.
https://doi.org/10.1016/j.rsase.2018.05....
; Mathew et al., 2017; Sannigrahi et al., 2018Sannigrahi, S., Bhatt, S., Rahmat, S., Uniyal, B., Banerjee, S., Chakraborti, S., Jha, S., Lahiri, S., Santra, K., & Bhatt, A. (2018). Analyzing the role of biophysical compositions in minimizing urban land surface temperature and urban heatingUrban Climate, 24, 803-819. https://doi.org/10.1016/j.uclim.2017.10.002.
https://doi.org/10.1016/j.uclim.2017.10....
; Singh et al., 2017). The nature of LST and NDVI varies due to the seasonal change of evaporation, precipitation, moisture content, air temperature, etc. But, time-series analysis of the monthly variation in the LST-NDVI relationship in a tropical Indian city is rare.

It is a necessary task to build a month-wise LST-NDVI correlation for the sustainable development of town and country planning. Thus, to determine the characteristic features of monthly variation of LST-NDVI correlation, Raipur City of India was selected as it is not under any kind of extreme climatic condition and it is a smart city with a rapid land conversion. Generally, the LST-NDVI correlation is negative on the tropical cities of similar environmental conditions of Raipur. But, the strength of the LST-NDVI relationship can change temporally, seasonally, and spatially. The relationship is changed with time as the land surface materials change with time. Elevation and slope are two main physiographic influencing factors that generate a negative correlation with LST. Wind speed and humidity are two climatic factors that create a negative relationship with LST. The relationship also depends on the LULC types as vegetation, soil, water, or built-up area change the values of NDVI as well as LST. Different seasons also play a significant role in the LST-NDVI relationship as the growth of vegetation and increase of LST primarily depend on seasonal change. But, no specific conclusion can draw between LST and NDVI by using a small number of remotely sensed data or within a short duration of research. A strong conclusion on the LST-NDVI relationship can be drawn only after the analyses of the multi-temporal and multi-seasonal data sets for a long-term continuous timeframe. Thus, large Landsat data sets are necessary to obtain a reliable result on this relationship. The present study analyzes the nature, strength, and trend of the effect of LST on NDVI and the LST-NDVI correlation on different types of LULC and their seasonal variation from 1988 to 2019. Thus, the new direction of the study is the long-term monthly change of LST-NDVI correlation analysis using the time-series data of Landsat sensors. The objective of the current research is to analyze the response of mean LST and LST-NDVI correlation in different months.

Study area and data

Figure 1 shows the research place (Raipur City of India) of the present research work. Figure 1(a) presents the outline map of India where Chhattisgarh State is located in the middle part (Source: Survey of India). Figure 1(b) presents the outline map of Chhattisgarh State with districts (Source: Survey of India). Figure 1(c) represents the false colour composite (FCC) image of Raipur City from recent Landsat 8 data (Date: 7 November 2018) where blue, green, and red bands of the image are filtered by the green, red, and infrared bands, respectively. False colour composite images are the combination of bands other than visible red, green, and blue as the red, green, and blue components of the display. These images are useful to allow us to distinguish various types of land surface materials that are difficult to identify by the naked eye or true colour composite image. Figure 1(d) indicates the contour map (Date: 11 October 2011) of the city from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM) data (Source: USGS). The city extends from 21o11'22"N to 21o20'02"N and from 81o32'20"E to 81o41'50"E. The total area of the city is approximately 164.23 km2. The only big river in the area is Mahanadi. The south of the city is covered by dense forests. Geologically the city is very stable and no such major geological hazards are seen in the area. The central part of the city has a higher elevation compared to the periphery area. According to India Meteorological Department (IMD), Raipur is under the savannah type of climate. Table 1 presents the climatic data of Raipur from 1981-2012 (Source: IMD). May is the hottest month followed by April, June, and March. July is the rainiest month followed by August, June, and September. October and November are the post-monsoon months that experience pleasant weather conditions. December (the coldest month), January, and February are the winter months. The pre-monsoon and winter months (including November) remain dry compared to the monsoon and post-monsoon months.

Table 1
Climate data for Raipur City (1981-2012)

Figure 1
Location of the study area (a) India (b) Chhattisgarh (c) FCC image of Raipur (d) Contour Map of Raipur showing the contours of 220 m, 240 m, 260 m, 280 m, 300 m, and 320 m.

Table 2 shows the resolution and wavelength information visible to near-infrared (VNIR) bands, shortwave infrared (SWIR) bands, and thermal infrared (TIR) bands of different types of Landsat satellite sensors.

Table 2
Band and wavelength information about various types of Landsat sensors

One hundred and eighteen available cloud-free Landsat TM, ETM+, and OLI/TIRS data from 1988 to 2019 were freely downloaded from the USGS Data Centre (Table 2) to conduct the whole study. OLI/TIRS dataset has two TIR bands (bands 10 and 11). This large dataset was prepared by taking eleven data from January, fifteen from February, thirteen from March, ten from April, seventeen from May, four from June, four from September, thirteen from October, fifteen from November, and sixteen from December. There are very few cloud-free data sets available in the wet season (June-September), and this phenomenon could have an impact on the result of the retrieved LST. These Landsat data sets passed over the Raipur City every day between 10:00 AM to 10:30 AM. Hence, there is a scope to retrieve the LST of the study area at a specific time every day. The TIR band 10 data (100 m resolution) was applied for the current research due to better-calibrated certainty (Guha et al., 2018Guha, S., Govil, H., Dey, A., & Gill, N. (2018). Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI/TIRS data in Florence and Naples city, Italy. Eur J Remote Sens, 51(1), 667-678. https://doi:10.1080/22797254.2018.1474494.
https://doi:10.1080/22797254.2018.147449...
). The TIR band 10 data was resampled to 30 m x 30 m pixel size by the USGS data centre. TM data has only one TIR band (band 6) of 120 m resolution that was also resampled to 30 m x 30 m pixel size by the USGS data centre. ETM+ data has a TIR band (band 6) of 60 m resolution, and it was again resampled to 30 m x 30 m pixel size by the USGS data centre. The spatial resolution of the VNIR bands of the three types of Landsat sensors is 30 m.

Table 3
Specification of TM, ETM+, and OLI/TIRS data used in the present study

Methodology

Retrieving LST from Landsat data

In this study, the mono-window algorithm was applied to retrieve LST from multi-temporal Landsat satellite sensors where three necessary parameters are ground emissivity, atmospheric transmittance, and effective mean atmospheric temperature (Qin et al., 2001Qin, Z., Karnieli, A., & Barliner, P. (2001). A Mono-WindowAlgorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region. Int J Remote Sens, 22(18), 3719-3746. https://doi:10.1080/01431160010006971.
https://doi:10.1080/01431160010006971...
; Wang et al., 2016Wang, J., Qingming, Z., Guo, H., & Jin, Z. (2016). Characterizing the spatial dynamics of land surface temperature-impervious surface fraction relationship. Int J Appl Earth Obs Geoinf, 45, Part-A, 55-65. https://doi.org/10.1016/j.jag.2015.11.006.
https://doi.org/10.1016/j.jag.2015.11.00...
; Wang et al., 2019; Sekertekin & Bonafoni, 2020Sekertekin, A., & Bonafoni, S. (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 Sens, 12(2), 294.). At first, the original TIR bands (100 m resolution for Landsat 8 OLI/TIRS data, 60 m resolution for Landsat 7 ETM+ data, and 120 m resolution for Landsat 5 TM data) were resampled into 30 m by the USGS data centre for further application.

The TIR pixel values are firstly converted into radiance from digital number (DN) values. Radiance for TIR band of Landsat 5 TM data and Landsat 7 ETM+ data is obtained using Eq. (1) (USGS):

L λ = [ L M A X λ L M I N λ Q C A L M A X Q C A L M I ] * [ Q C A L Q C A L M I N ] + L M N λ (1)

where, Lλ is Top of Atmosphere (TOA) spectral radiance (Wm-2sr-1mm-1), QCAL is the quantized calibrated pixel value in DN, LMIN (Wm-2sr-1mm-1) is the spectral radiance scaled to QCALMIN, LMAXλ (Wm-2sr-1mm-1) is the spectral radiance scaled to QCALMAX, QCALMIN is the minimum quantized calibrated pixel value in DN and QCALMAX is the maximum quantized calibrated pixel value in DN. LMINλ, LMAXλ, QCALMIN, and QCALMAXvalues are obtained from the metadata file of Landsat TM and ETM+ data. Radiance for Landsat 8 TIR band is obtained from Eq. (2) (Zanter, 2019Zanter, K. (2019). Landsat 8 (L8) Data Users Handbook. EROS, Sioux Falls, SD, USA.):

L λ = M L Q C A L + A L (2)

where, Lλ is the TOA spectral radiance (Wm-2sr-1mm-1), ML is the band-specific multiplicative rescaling factor from the metadata, AL is the band-specific additive rescaling factor from the metadata, QCALis the quantized and calibrated standard product pixel values (DN). All of these variables can be retrieved from the metadata file of Landsat 8 OLI/TIRS data.

For Landsat 5 TM data and Landsat 7 ETM+ data, the reflectance value is obtained from radiances using Eq. (3) (USGS):

ρ λ = π L λ d 2 E S U N λ cos θ s (3)

where, ρλis unitless planetary reflectance, Lλ is the TOA spectral radiance (Wm-2sr-1µm-1), dis Earth-Sun distance in astronomical units, ESUNλ is the mean solar exo-atmospheric spectral irradiances (Wm-2µm-1) and θs is the solar zenith angle in degrees. ESUNλ values for each band of Landsat 5 can be obtained from the handbooks of the related mission. θs and dvalues can be attained from the metadata file.

For Landsat 8 OLI/TIRS data, reflectance conversion can be applied to DN values directly as in Eq. (4) (Zanter, 2019Zanter, K. (2019). Landsat 8 (L8) Data Users Handbook. EROS, Sioux Falls, SD, USA.):

ρ λ = M ρ Q C A L + A ρ sin θ S E (4)

where, Mρ is the band-specific multiplicative rescaling factor from the metadata, Aρis the band-specific additive rescaling factor from the metadata, QCAL is the quantized and calibrated standard product pixel values (DN) and θSEis the local sun elevation angle from the metadata file.

Eq. (5) is used to convert the spectral radiance to at-sensor brightness temperature (Wukelic et al., 1989Wukelic, G. E., Gibbons, D. E., Martucci, L. M., & Foote, H. P. (1989). Radiometric calibration of Landsat Thematic Mapper thermal band. Remote Sens Environ, 28, 339-347.).

T b = K 2 ln ( K c L 2 + 1 ) (5)

where, Tbis the brightness temperature in Kelvin (K), Lλ is the spectral radiance in Wm-2sr-1mm-1; K2and K1are calibration constants. For Landsat 8 data, K1is 774.89, K2is 1321.08 (Wm-2sr-1mm-1). For Landsat 7 data, K1= 666.09, K2= 1282.71 (Wm-2sr-1mm-1). For Landsat 5 data, K1is 607.76, K2is 1260.56 (Wm-2sr-1mm-1).

The land surface emissivityε, is estimated using the NDVI Thresholds Method (Sobrino et al., 2004Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from Landsat TM5. Remote Sens Environ,90(4), 434-440. https://doi.org/10.1016/j.rse.2004.02.003.
https://doi.org/10.1016/j.rse.2004.02.00...
). The fractional vegetationFv, of each pixel, is determined from the NDVI using the following equation (Carlson & Ripley, 1997Carlson, T. N., & Ripley, D. A. (1997). On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index. Remote Sens Environ, 62(3), 241-252. https://doi.org/10.1016/S0034-4257(97)00104-1.
https://doi.org/10.1016/S0034-4257(97)00...
):

F v = ( N D V I N D V I min N D V I max N D V I min ) 2 (6)

where, NDVImin is the minimum NDVI value (0.2) for bare soil pixel and NDVImax is the maximum NDVI value (0.5) for healthy vegetation pixel.

dεis the effect of the geometrical distribution of the natural surfaces and internal reflections. For mixed and elevated land surfaces, the value of dεmay be 2% (Sobrino et al., 2004Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from Landsat TM5. Remote Sens Environ,90(4), 434-440. https://doi.org/10.1016/j.rse.2004.02.003.
https://doi.org/10.1016/j.rse.2004.02.00...
).

d ε = ( 1 ε s ) ( 1 F v ) F ε v (7)

where, εv is vegetation emissivity, εsis soil emissivity, Fvis fractional vegetation, Fis a shape factor whose mean is 0.55 (Sobrino et al., 2004Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from Landsat TM5. Remote Sens Environ,90(4), 434-440. https://doi.org/10.1016/j.rse.2004.02.003.
https://doi.org/10.1016/j.rse.2004.02.00...
).

ε = ε v F v + ε s ( 1 F v ) + d ε (8)

where,ε is the land surface emissivity that is determined by the following equation (Sobrino et al., 2004Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from Landsat TM5. Remote Sens Environ,90(4), 434-440. https://doi.org/10.1016/j.rse.2004.02.003.
https://doi.org/10.1016/j.rse.2004.02.00...
):

ε = 0.004 * F v + 0.986 (9)

Water vapour content is estimated by the following equation (Li, 2006Li, J. (2006). Estimating land surface temperature from Landsat-5 TM. Remote Sens Technol Appl,21, 322-326.; Yang & Qiu, 1996Yang, J., & Qiu, J. (1996). The empirical expressions of the relation between precipitable water and ground water vapor pressure for some areas in China. Sci Atmos Sinica, 20, 620-626.):

w = 0.0981 * [ 10 * 0.6108 * exp ( 17.27 * ( T 0 273.15 ) 237.3 + ( T 0 273.15 ) ) * R H ] + 0.1697 (10)

where, wis the water vapour content (g/cm2), T0is the near-surface air temperature in Kelvin (K), RH is the relative humidity (%). These parameters of the atmospheric profile are obtained from the Meteorological Centre, Raipur (http://www.imdraipur.gov.in). Atmospheric transmittance is determined for Raipur City using the following equation (Qin et al., 2001Qin, Z., Karnieli, A., & Barliner, P. (2001). A Mono-WindowAlgorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region. Int J Remote Sens, 22(18), 3719-3746. https://doi:10.1080/01431160010006971.
https://doi:10.1080/01431160010006971...
; Sun et al., 2010Sun, Q., Tan, J., & Xu, Y. (2010). 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, 59, 1047-1055. https://doi.org/10.1007/s12665-009-0096-3.
https://doi.org/10.1007/s12665-009-0096-...
):

τ = 1.031412 0.11536 w (11)

where, τis the total atmospheric transmittance, εis the land surface emissivity.

Raipur City is located in the tropical region. Thus, the following equations are applied to compute the effective mean atmospheric transmittance of Raipur (Qin et al., 2001Qin, Z., Karnieli, A., & Barliner, P. (2001). A Mono-WindowAlgorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region. Int J Remote Sens, 22(18), 3719-3746. https://doi:10.1080/01431160010006971.
https://doi:10.1080/01431160010006971...
; Sun et al., 2010Sun, Q., Tan, J., & Xu, Y. (2010). 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, 59, 1047-1055. https://doi.org/10.1007/s12665-009-0096-3.
https://doi.org/10.1007/s12665-009-0096-...
):

T a = 17.9769 + 0.91715 T 0 (12)

LST is retrieved from Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS satellite data by using the following equations (Qin et al., 2001Qin, Z., Karnieli, A., & Barliner, P. (2001). A Mono-WindowAlgorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region. Int J Remote Sens, 22(18), 3719-3746. https://doi:10.1080/01431160010006971.
https://doi:10.1080/01431160010006971...
):

T s = [ a ( 1 C D ) + ( b ( 1 C D ) + C + D ) T b D T a ] C (13)

C = ε τ (14)

D = ( 1 τ ) [ 1 + ( 1 ε ) τ ] (15)

where, εis the land surface emissivity, τis the total atmospheric transmittance, Tbis the at-sensor brightness temperature, Tais the mean atmospheric temperature, T0is the near-surface air temperature, Tsis the land surface temperature, a=67.355351, b=0.458606.

Figure 2 shows the flowchart of methodology of the present study which clearly presents the steps of the LST retrieval process.

Figure 2
Flowchart showing the methodology of the present study.

Determination of NDVI

Here, NDVI was selected as a normalized difference spectral index in the research work (Tucker, 1979Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ, 8(2), 127-150.). NDVI is determined by the red and NIR bands. For TM and ETM+ data, band 3 is used as a red band and band 4 is used as a NIR band, respectively. For OLI/TIRS data, band 4 and band 5 are used as red and NIR bands, respectively (Table 4). The value of NDVI ranges between −1 and +1. Generally, the negative value of NDVI indicates the water surfaces. Positive NDVI shows vegetation surface. The increasing positive value of NDVI indicates the increase of greenness in plants.

Table 4
Description of normalized difference vegetation index (NDVI)

Results and discussion

Monthly variation in LST distribution

Table 5 shows a clear observation of monthly change in the mean LST values. In this table, the mean LST of each image is shown. The mean LST of every month was also determined. In this way, the mean LST values for ten months (no cloud-free data was available for July and August) were presented.

It is seen from Table 5 that the mean LST of the city was above 32oC mean LST for all the months from March-May of 1992, 2001-02, 2004-05, 2008-11, 2013, 2016-17, and 2019. June and September of 2005, 2006, and 2009 have more mean LST values than the earlier and later years. The scenario was completely different from October to February, where that the mean LST of the city was below 28oC LST. April (38.79oC mean LST), May (36.65oC mean LST), June (34.56oC mean LST), and March (32.11oC mean LST) - these four months have an average value of > 35oC mean LST throughout the entire period of study. February (27.88oC mean LST), October (27.23oC mean LST), September (27.18oC mean LST), and November (25.83oC mean LST) - these four months have an average value of 25-28oC mean LST throughout the entire time. Only December (23.76oC mean LST) and January (23.01oC mean LST) months have an average value of < 24oC mean LST for the period. The average value of the highest and the lowest mean LST from 1988 to 2019 is observed in April and January, respectively. The northwest and southeast parts of the study area exhibit high LST. These parts also have a low percentage of urban vegetation and a high percentage of built-up areas and bare land. It shows that the proportion of vegetation reduced significantly with time.

Figure 3 shows the line graph of LST in different months from 1988 o 2019. October, November, and December present an almost similar pattern of LST distribution. January and February show an almost similar trend in their LST graph. A similarity also has been seen in the mean LST graph of March, April, and May. The LST graph of June shows a negative trend, while September shows a positive trend.

Table 5
Monthly distribution of mean LST (oC) for the entire Raipur City from 1988 to 2019

Figure 3
Line graphs for monthly variation of LST (oC) from1988 to 2019: (a) January (b) February (c) March (d) April (e) May (f) June (g) September (h) October (i) November (j) December.

Monthly variation on LST-NDVI relationship

Table 6
Monthly variation of LST-NDVI correlation coefficient(1988-2019) (significant at 0.05 level)

Table 6 represents a monthly variation of Pearson's linear correlation method between LST and NDVI significant at 0.05 level. On average, October (-0.62), September (-0.55), and April (-0.51) months build a strong negative correlation. June (-0.47), May (-0.44), March (-0.44), and November (-0.39) months have a moderate negative correlation. A weak negative correlation was found in February (-0.29), January (-0.24), and December (-0.21) months. The main reason behind the strong to moderate LST-NDVI correlation in March-November is the presence of high intensity of moisture and chlorophyll content in green vegetation. Dry months reduce the strength of regression, while wet months enhance the strength of the LST-NDVI regression. Hence, the climatic condition and surface material influence the LST-NDVI correlation analysis.

Figure 4
Line graphs for monthly variation of LST-NDVI relationship (1988-2019): (a) January (b) February (c) March (d) April (e) May (f) June (g) September (h) October (i) November (j) December.

Figure 4 shows the line graphs for monthly variation of LST-NDVI relationships. June and September months show a smooth convex and concave trend, respectively due to wet weather. The line graphs of October, November, and December show more fluctuation due to more differences in atmospheric components. The line graphs of January and March look almost similar. February, April, and May present similar trends of LST.

Liang et al. (2012Liang, B. P., Li, Y., & Chen, K. Z. (2012). A Researchon Land Features and Correlation between NDVI and Land Surface Temperature in Guilin City. Remote Sens Tech Appl, 27, 429-435.) presented similar types of negative NDVI-LST correlation in Guilin City, China. In high latitudes, positive LST-NDVI relationships have been observed as the presence of vegetation increases the value of LST in high latitudes where the winter season is severe (Karnieli et al., 2006Karnieli, A., Bayasgalan, M., Bayarjargal, Y., Agam, N., Khudulmur, S., & Tucker, C. J. (2006). Comments on the use of the Vegetation Health Index over Mongolia. Int J Remote Sens, 27(10), 2017-2024. https://doi:10.1080/01431160500121727.
https://doi:10.1080/01431160500121727...
). Yue et al. (2007Yue, W., Xu, J., Tan, W., & Xu, L. (2007). The Relationship between Land Surface Temperature and NDVI with Remote Sensing. Application to Shanghai Landsat 7 ETM+ data. Int J Remote Sens, 28, 3205-3226. https://doi.org/10.1080/01431160500306906.
https://doi.org/10.1080/0143116050030690...
) showed that the LST-NDVI relationship in Shanghai City, China was negative and was different in different LULC types like the relationship was strong negative on vegetation. Sun and Kafatos (2007Sun, D., & Kafatos, M. (2007). Note on the NDVI-LST Relationship and the Use of Temperature-Related Drought Indices over North America. Geophys Res Lett, 34(24), L24406. http://doi.org/10.1029/2007GL031485.
http://doi.org/10.1029/2007GL031485...
) stated that the LST-NDVI correlation was positive in the winter season while it was negative in the summer season as the winter season produces the lowest LST on the rock surface and dry soil and vice-versa in the summer season. This relationship was also negative in Mashhad, Iran as it is a dry tropical city (Gorgani et al., 2013Gorgani, S. A., Panahi, M., & Rezaie, F. (2013). The relationship between NDVI and LST in the Urban area of Mashhad, Iran. In International Conference on Civil Engineering Architecture and Urban Sustainable Development. November, Tabriz, Iran.). The relationship was strongly negative in Berlin City for any season (Marzban et al., 2018Marzban, F., Sodoudi, S., & Preusker, R. (2018). The influence of land-cover type on the relationship between LST-NDVI and LST-Tair. Int J Remote Sens, 39(5), 1377-1398. https://doi.org/10.1080/01431161.2017.1462386.
https://doi.org/10.1080/01431161.2017.14...
). This correlation tends to be more negative with the increase of surface moisture as in the wet season more green and healthy vegetation is produced (Moran et al., 1994Moran, M. S., Clarke, T. R., Inouie, Y., & Vidal, A. (1994). Estimating Crop Water-Deficit using the Relation between Surface Air-Temperature and Spectral Vegetation Index. Remote Sens Environ, 49, 246-263. https://doi.org/10.1016/0034-4257(94)90020-5.
https://doi.org/10.1016/0034-4257(94)900...
; Lambin & Ehrlich, 1996Lambin, E. F., & Ehrlich, D. (1996). The Surface Tenperature-Vegetation Index Space for Land Use and Land Cover Change analysis. Int J Remote Sens, 17, 463-487. https://doi.org/10.1080/01431169608949021.
https://doi.org/10.1080/0143116960894902...
; Prehodko & Goward, 1997Prehodko, L., & Goward, S. N. (1997). Estimation of Air Temperature from Remotely Sensed Surface Observations. Remote Sens Environ, 60, 335-346. https://doi:10.1016/S0034-4257 (96)00216-7.
https://doi:10.1016/S0034-4257 (96)00216...
; Sandholt et al., 2002Sandholt, I., Rasmussen, K., & Andersen, J. (2002). A simple interpretation of the Surface Temperature/Vegetation Index Space for Assessment of Surface Moisture Status. Remote Sens Environ, 79, 213-224. https://doi.org/10.1016/s0034-4257(01)00274-7.
https://doi.org/10.1016/s0034-4257(01)00...
). The present study also found a negative LST-NDVI correlation for all the months as it is a humid tropical city. The value of the correlation coefficient is inversely related to the surface moisture content, i.e., the negativity of the relationship increases with the increase of surface moisture content.

Conclusions

The present study estimates the monthly variation of LST distribution in Raipur City, India using one hundred and eighteen Landsat datasets from 1988 to 2019. April, May, June, and March present higher LST values than the rest of the months. The present study also assesses the monthly correlation of LST and NDVI in Raipur City. In general, the results show that LST is inversely related to NDVI, irrespective of any month. From March to November, the correlation is strong to moderate negative, whereas it is found weak negative in the winter season (December to February). The presence of healthy green plants and high moisture content in the air is the main responsible factors for strong negativity. The study is useful for the environmentalists and urban planners for future ecological planning from several points of view. Special attention may be taken in March, April, May, and June to increase the negativity of the relationship by plantation. Simultaneously, more trees can be planted in December, January, and February for generating pleasant weather. Moreover, some commercial activities may be decreased in the winter months as at that time the city remains dry and more polluted. Special emphasis should be taken on the transport and industrial sectors as these sectors are mainly responsible for generating high LST. Mass transport system must be encouraged instead of the private transport system. The area under the park, urban vegetation, water bodies, and wetland must be increased at any cost as these changes can bring ecological comfort to the city. The unused fallow lands should be converted into vegetation and water area. It will be beneficial to the residents if any public and private initiatives have to be taken on seasonal plantation programmes along the roadways and barren lands.

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Editor:

Fábio Duarte

Publication Dates

  • Publication in this collection
    29 Nov 2021
  • Date of issue
    2021

History

  • Received
    25 Sept 2020
  • Accepted
    30 June 2021
Pontifícia Universidade Católica do Paraná Rua Imaculada Conceição, 1155. Prédio da Administração - 6°andar, 80215-901 - Curitiba - PR, 55 41 3271-1701 - Curitiba - PR - Brazil
E-mail: urbe@pucpr.br