Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India

Shahfahad, .; Kumari, Babita; Tayyab, Mohammad; Ahmed, Ishita Afreen; Baig, Mirza Razi Imam; Khan, Mohammad Firoz

doi:10.1007/s12517-020-06068-1

Cited by 49 publications

(17 citation statements)

References 70 publications

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“…The rapid urbanization in many developing countries over the past decades seems to have been accompanied by extensive land use and land cover changes, which potentially affect local or regional climate through altering the surface energy and water balances 1 – 6 . One of the major consequences of these modifications is the increase in land surface temperature (LST) in urban areas, which strengthens the urban heat island (UHI) effect 7 , 8 . The UHI is a localized climate phenomenon whereby urban areas experience warmer temperatures than their surrounding non-urban areas 9 , 10 .…”

Section: Introductionmentioning

confidence: 99%

Evaluating contributions of urbanization and global climate change to urban land surface temperature change: a case study in Lagos, Nigeria

Guo

Zhang

et al. 2022

Sci Rep

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This study develops a general method to evaluate the contributions of localized urbanization and global climate change to long-term urban land surface temperature (ULST) change. The method is based on the understanding that long-term annual ULST is controlled by three factors: (1) localized urbanization, (2) global climate change, and (3) interannual climate variation. Then the method removes the interannual climate fluctuations on long-term observed LST time series via linear regression and separates the contributions of urbanization and climate change to the impacts on long-term ULST via urban–rural comparison. The method is applied to Lagos, a fast-growing metropolis in the tropical West Africa, as an example for reference. Combined time-series daily daytime and nighttime MODIS Land Surface Temperature (LST) data over the years of 2003–2021 are used as the representation of land surface temperature. To avoid the potentioal interannual data biase due to uneven availability of data in the rainy seasons over years, only MODIS LST data from dry seasons are used in the study. The results are summarized as follows for Lagos: (1) long-term annual ULST is confirmed to be controlled by the three factors; (2) the proposed method can separate the contribution of the three factors to the ULST; (2) both localized urbanization and global warming are verified to contribute to the ULST increase with positive trends; (3) daytime ULST increased the most in the afternoon time at a mean rate of 1.429 °C per decade, with 0.985 °C (10 year)−1 contributed by urbanization and 0.444 °C (10 year)−1 contributed by climate warming; (4) nighttime ULST in Lagos increased the most after midnight at a rate of 0.563 °C (10 year)−1, with 0.56 °C (10 year)−1 contributed by urbanization and 0.003 °C (10 year)−1 contributed by climate warming; and (5) urbanization is generally responsible for around 60.97% of the urban warming in Lagos. Therefore, the increasing urbaniztion-induced urban heat island effect is the major cause for more heat-related health risks and climate extremes that many urban residents are suffering. The results of this study are of useful reference for both urbanization and climate change related issues in the geo-science field.

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Section: Introductionmentioning

confidence: 99%

Evaluating contributions of urbanization and global climate change to urban land surface temperature change: a case study in Lagos, Nigeria

Guo

Zhang

et al. 2022

Sci Rep

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“…Among all methods, four are mentionable: Mono Window Algorithm (MWA) (Qin et al, 2001;Kumari et al, 2020), Single Channel Algorithm (SCA) (Dong et al, 2017), Radiative Transfer Remote Sensing 2020, 12, 294 5 of 32 Equation (RTE) method (Saratoon et al, 2013), and Split Window Algorithm (SWA) (Rozenstein et al, 2014;Wang et al, 2019) using Landsat 5 Thematic Mapper (TM), 7 Enhanced Thematic Mapper Plus (ETM+), and 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data (García-Santos et al, 2018;Hao et al, 2019). In this study, the MWA method (Qin et al, 2001;Guha et al, 2019;Guha and Govil, 2020;Kumari et al, 2020) was chosen as it is easily applicable to Landsat 5 TM, 7 ETM+ and 8 OLI/TIRS data. Mono-window algorithm method is different from SWA method and more suitable to apply as it retrieves LST from only one thermal band data.…”

Section: Retrieving Land Surface Temperaturementioning

confidence: 99%

“…Radiative Transfer Equation (RTE) method (Saratoon et al, 2013), Single Channel Algorithm (SCA) (Dong et al, 2017), and Mono Window Algorithm (MWA) (Qin et al, 2001;Kumari et al, 2020) are the several methods for LST retrieval using Landsat 5 Thematic Mapper (TM), 7 Enhanced Thematic Mapper Plus (ETM+), and 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data (García-Santos et al, 2018;Hao et al, 2019). In this study, Mono Window Algorithm (MWA) was evaluated for Landsat 5, 7, and 8 imageries.…”

Section: Introductionmentioning

confidence: 99%

A Time Series Analysis of Forest Cover and Land Surface Temperature Change Over Dudpukuria-Dhopachari Wildlife Sanctuary Using Landsat Imagery

Hasnat

2021

Front. For. Glob. Change

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Forest cover change is an important criterion as it affects the environmental balance whereas land surface temperature is a significant parameter within the earth climate system. Spatio-temporal change of forest cover can be detected and land surface temperature can be retrieved by applying remote sensing technology. The present study aimed to capture the impact of forest cover change on land surface temperature in Dudpukuria-Dhopachari Wildlife Sanctuary (DDWS), Bangladesh, using multi-spectral and multi-temporal satellite data. To avoid the biasness in the calculation, leaf flash time was targeted for collecting Landsat images from United States Geological Survey (USGS) Earth Explorer and, based on availability, images were collected purposively which ones had closer time period:1990 (March 5, 1990), 2000 (February 5, 2000), 2010 (February 24, 2010) and 2020 (March 23, 2020). Unsupervised classification was applied over the images Landsat 4–5 Thematic Mapper (TM), 7 Enhanced Thematic Mapper Plus (ETM+), and 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data for detecting forest cover change. To retrieve the land surface temperature, Mono Window Algorithm (MWA) method was applied over similar images. Maximum forest degradation was observed in 2010 and the change found was 17% as compared to 1990. After 2010, the forest started to flourish. Land surface temperature dramatically changes over the time period. The highest land surface temperature in the forested area was observed in 2020 (32.2°C) and it was changed 7.7°C from that of the 1990 (24.5°C). In every 10 years, almost 2.3°C–3.0°C temperature change was detected. In the first three decades, a reverse relationship was observed between land surface temperature and forest cover; however, in the last decade, land surface temperature was found to increase with the increase of forest cover. Thus, the results of the study revealed that land surface temperature may not be relevant with the local forest cover change directly. It can be estimated from the results that local forest cover change may have limited impact on local temperature rather than global forest cover change, whereas global warming could play a vital role in changing land surface temperature locally as well as globally.

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“…Nevertheless, the rapid development of remote sensing techniques has enabled satellite images to acquire near-real-time data on detailed spatial surfaces. Therefore, to obtain high-resolution, consistent, and repetitive LST at the regional and global scales, remote sensing is the only practical and feasible way [ 8 , 9 , 10 , 11 , 12 ]. Many satellites carry thermal infrared sensors, such as MODIS/Terra and Aqua, advanced very high-resolution radiometer (AVHRR)/NOAA, and Landsat-8/TIRS [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Retrieval Algorithms of Land Surface Temperature from Landsat-8 Data: A Case Study of Shanghai, China

Yue

Lin

2021

IJERPH

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In the trend of global warming and urbanization, frequent extreme weather has a severe impact on the lives of citizens. Land Surface Temperature (LST) is an essential climate variable and a vital parameter for land surface processes at local and global scales. Retrieving LST from global, regional, and city-scale thermal infrared remote sensing data has unparalleled advantages and is one of the most common methods used to study urban heat island effects. Different algorithms have been developed for retrieving LST using satellite imagery, such as the Radiative Transfer Equation (RTE), Mono-Window Algorithm (MWA), Split-Window Algorithm (SWA), and Single-Channel Algorithm (SCA). A case study was performed in Shanghai to evaluate these existing algorithms in the retrieval of LST from Landsat-8 images. To evaluate the estimated LST accurately, measured data from meteorological stations and the MOD11A2 product were used for validation. The results showed that the four algorithms could achieve good results in retrieving LST, and the LST retrieval results were generally consistent within a spatial scale. SWA is more suitable for retrieving LST in Shanghai during the summer, a season when the temperature and the humidity are both very high in Shanghai. Highest retrieval accuracy could be seen in cultivated land, vegetation, wetland, and water body. SWA was more sensitive to the error caused by land surface emissivity (LSE). In low temperature and a dry winter, RTE, SWA, and SCA are relatively more reliable. Both RTE and SCA were sensitive to the error caused by atmospheric water vapor content. These results can provide a reasonable reference for the selection of LST retrieval algorithms for different periods in Shanghai.

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Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India

Cited by 49 publications

References 70 publications

Evaluating contributions of urbanization and global climate change to urban land surface temperature change: a case study in Lagos, Nigeria

Evaluating contributions of urbanization and global climate change to urban land surface temperature change: a case study in Lagos, Nigeria

A Time Series Analysis of Forest Cover and Land Surface Temperature Change Over Dudpukuria-Dhopachari Wildlife Sanctuary Using Landsat Imagery

A Comparative Analysis of Retrieval Algorithms of Land Surface Temperature from Landsat-8 Data: A Case Study of Shanghai, China

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