Split-Window Algorithm for Retrieval of Land Surface Temperature Using Landsat 8 Thermal Infrared Data

Rongali, Gopinadh; Keshari, Ashok K.; Gosain, A. K.; Khosa, Rakesh

doi:10.1007/s41651-018-0021-y

Cited by 64 publications

(37 citation statements)

References 52 publications

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“…It was found that the annual daytime SUHII values of these cities exhibited significant and positive relationships with the population (R 2 = 0.51, p < 0.05) and urban area (R 2 = 0.47, p < 0.05). Meanwhile, the altitude has little effect on the SUHII [58][59][60]. In this study, vegetation and anthropogenic heat emissions played a larger role in interpreting variations in the daytime and night-time SUHII values, respectively.…”

Section: The Effects Of Each Factor On Suhiimentioning

confidence: 69%

Investigating Surface Urban Heat Islands in South America Based on MODIS Data from 2003–2016

Wang

Yao

et al. 2019

Remote Sensing

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Surface urban heat islands (SUHIs) have been investigated in many regions around the world, but little attention has been given with regard to SUHIs in South America. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data was used to investigate the diurnal, seasonal, and interannual variations in the SUHI intensity (SUHII, the urban LST minus the rural LST) in 44 South American cities in different climate zones and types of rural land. To examine the effects of factors that may influence the SUHII, correlations between the SUHII and the enhanced vegetation index (EVI), urban area, population, altitude, and anthropogenic heat emissions were performed. The results showed that the SUHI effect was obvious in South America. The mean daytime SUHII was higher than the mean night-time SUHII in all areas except for the arid climate zone. In the daytime, the summer displayed a stronger SUHII in the warm temperate climate zone than the other seasons. The night-time SUHII showed less obvious seasonal variations. In addition, the surrounding land cover influenced the SUHII. During the day, the SUHII was therefore stronger in rural areas that were covered by forests than in other types of rural land. Interannually, most cities showed an insignificant temporal trend in the SUHII from 2003 to 2016. The daytime SUHII was significantly and negatively correlated with the ∆EVI (the urban EVI minus the rural EVI) across the 44 cities, but a poor relationship was observed at night. In addition, anthropogenic heat emissions were positively correlated with the night-time SUHII. Urban area, population, and altitude were weakly correlated with the SUHII, which suggested that these factors may not have a significant impact on the spatial variations in the SUHII in South America.

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Section: The Effects Of Each Factor On Suhiimentioning

confidence: 69%

Investigating Surface Urban Heat Islands in South America Based on MODIS Data from 2003–2016

Wang

Yao

et al. 2019

Remote Sensing

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“…The SW algorithm is based on the different atmospheric absorption behavior of two ra- diometric channels within the 10 -12.5µm window region (Rongali et al 2018). The basis of the SW algorithm is the radiance attenuation for atmospheric absorption, which is proportional to the radiance difference of simultaneous measurements at two different wavelengths, each of them being subject to varying amounts of atmospheric absorption (McMillin 1975;Rongali et al 2018). According to this algorithm, LST can be determined by the following formula (Jiménez-Muñoz et al 2014): Where:…”

Section: Study Area and Materialsmentioning

confidence: 99%

“…A number of algorithms have been used to estimate the LST using remote sensing thermal infrared (TIR) data as it is capable to decipher the thermal characteristic of the land surface. These algorithms are namely mono-window (MW), split-window (SW), dual-angle (DA), single-channel (SC)… (Galve et al 2008;Rongali et al 2018). The studies carried out in different areas, such as the northern Negev Desert, Israel (Du et al 2014;Rozenstein et al 2014) and the Beas River basin, India (Rongali et al, 2018) show that the split-window algorithm can be adjusted for estimating LST from Landsat 8 data to get better accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms are namely mono-window (MW), split-window (SW), dual-angle (DA), single-channel (SC)… (Galve et al 2008;Rongali et al 2018). The studies carried out in different areas, such as the northern Negev Desert, Israel (Du et al 2014;Rozenstein et al 2014) and the Beas River basin, India (Rongali et al, 2018) show that the split-window algorithm can be adjusted for estimating LST from Landsat 8 data to get better accuracy. The objective of our paper is to evaluate the dynamics of LST in Loc Ninh district, Binh Phuoc province of Southeast region of Vietnam during 2015 -2016 dry season using Landsat 8 multi-temporal data.…”

Section: Introductionmentioning

confidence: 99%

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Land Surface Temperature Dynamics In Dry Season 2015-2016 According To Landsat 8 Data In The South-East Region of Vietnam

Nguyen

Hung

Dao

et al. 2019

Geogr. environ. sustain.

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Located in Southeast Asia, Vietnam is one of the most severely affected countries by climate change and faces to series of challenges related to climate change, in which droughts are one of the most serious natural disasters. Land surface temperature (LST) is important factor in evaluating soil moisture and drought phenomenon. Remote sensing technique with many advantages, compared with traditional methods, can be used effectively for retrieving LST. This article presents study on the application of LANDSAT 8 multi-temporal data for monitoring LST changes in dry season 2015-2016 in Loc Ninh district, Binh Phuoc province in Southeast region of Vietnam. LST was derived using Split-Window (SW) algorithm. The results showed that the LST at the end of 2015-2016 dry seasons (in February and March) is much higher than at the early of dry season. The area with LST higher than 309 K increases very fast in dry season 2015-2016, from less than 1% of the total study area in November and December to 19.59% in February and 30.74% in March. The results obtained in this study can be used to create the LST distribution map and to monitor drought phenomenon.

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“…T a describes the kinetic energy of the near‐surface atmosphere and has a significant influence on evaporation, humidity, wind, and precipitation types. T s is found to differ with T a with respect to both physical meaning and magnitude (Jin and Dickinson, 2010; Rongali et al ., 2018) and is controlled by downward terrestrial radiation, and therefore, by surface heat flux exchanges with the atmosphere. The land–atmosphere coupling is extremely complex and variable in both space and time, and its strength is often linked with extreme climate events such as heat weaves, droughts, and heavy rainfall, and so forth (Seneviratne et al ., 2010; Zhang and Wu, 2011), which impact heavily on social life and economical activities.…”

Section: Introductionmentioning

confidence: 99%

A climatology of surface–air temperature difference over the Tibetan Plateau: Results from multi‐source reanalyses

Wang

Chen

Pang

et al. 2020

Intl Journal of Climatology

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The Tibetan Plateau (TP), known as earth's “Third Pole,” influences regional and even global weather and climate systems through its mechanical and thermal‐dynamical forcing. Near‐surface (2 m) air temperature (Ta) and surface (skin) temperature (Ts) are two crucial parameters of land–atmosphere interactions and climate change. Their difference (ΔT = Ts − Ta) determines the heating source over the TP that drives the Asian summer monsoon. This study focuses on climatology, inter‐annual variability, and long‐term trend of ΔT over the TP in the last four decades (1979–2018), based on four latest reanalysis datasets including ERA‐Interim, ERA5, MERRA2, and JRA55, along with observational data. We show that ΔT‐based different datasets display fairly different climatology in terms of seasonality, spatial distribution, and long‐term trend. ΔT exhibits a clear seasonality with negative value in winter and positive ones in summer despite different strengths and timings presented by the reanalyses. Along with global warming, all reanalyses except JRA55 exhibit obvious downwards trends of ΔT in a spatially non‐uniform way. The median ΔT among the four reanalyses features uniform decreases in all seasons, with the most distinct area on the northern TP, as well as the largest and least decreases in autumn and spring, respectively. Further analysis shows that the differences in ΔT are most likely associated with discrepancies in radiation forcing, snow cover, wind speed, and boundary layer height within the reanalyses. The present findings highlight the difficulty for the state‐of‐the‐art reanalyses to represent the climate change over the TP and point to possible factors behind the deficiencies identified.

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Split-Window Algorithm for Retrieval of Land Surface Temperature Using Landsat 8 Thermal Infrared Data

Cited by 64 publications

References 52 publications

Investigating Surface Urban Heat Islands in South America Based on MODIS Data from 2003–2016

Investigating Surface Urban Heat Islands in South America Based on MODIS Data from 2003–2016

Land Surface Temperature Dynamics In Dry Season 2015-2016 According To Landsat 8 Data In The South-East Region of Vietnam

A climatology of surface–air temperature difference over the Tibetan Plateau: Results from multi‐source reanalyses

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