Exploratory Analysis of Urban Climate Using a Gap-Filled Landsat 8 Land Surface Temperature Data Set

The Landsat 8 satellites have retrieved land surface temperature (LST) resampled at a 30-m spatial resolution since 2013, but the urban climate studies frequently use a limited number of images due to the problems related to missing data over the city of interest. This paper endorses a procedure for building a long-term gap-free LST data set in an urban area using the high-resolution Landsat 8 imagery. The study is applied on 94 images available through 2013–2018 over Bucharest (Romania). The raw images containing between 1.1% and 58.4% missing LST data were filled in using the Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm implemented in the sinkr R packages. The resulting high-spatial-resolution gap-filled land surface temperature data set was used to explore the LST climatology over Bucharest (Romania) an urban area, at a monthly, seasonal, and annual scale. The performance of the gap-filling method was checked using a cross-validation procedure, and the results pledge for the development of an LST-based urban climatology.

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A daily merged MODIS Aqua–Terra land surface temperature data set for the conterminous United States

Remote Sensing of Environment ◽

10.1016/j.rse.2011.12.019 ◽

2012 ◽

Vol 119 ◽

pp. 315-324 ◽

Cited By ~ 55

Author(s):

William L. Crosson ◽

Mohammad Z. Al-Hamdan ◽

Sarah N.J. Hemmings ◽

Gina M. Wade

Keyword(s):

United States ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Temperature Data ◽

Data Set ◽

Surface Temperature Data

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Development of a 250-m Downscaled Land Surface Temperature Data Set and Its Application to Improving Remotely Sensed Evapotranspiration Over Large Landscapes in Northern China

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.3037168 ◽

2020 ◽

pp. 1-12

Author(s):

Kai Liu ◽

Hongbo Su ◽

Xueke Li ◽

Shaohui Chen

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Northern China ◽

Remotely Sensed ◽

Temperature Data ◽

Data Set ◽

Surface Temperature Data

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SPATIAL AND TEMPORAL ANALYSIS OF MONTHLY WATER CONSUMPTION AND LAND SURFACE TEMPERATURE (LST) DERIVED USING LANDSAT 8 AND MODIS DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w19-193-2019 ◽

2019 ◽

Vol XLII-4/W19 ◽

pp. 193-198

Author(s):

R. Enriquez ◽

M. Rodriguez ◽

A. C. Blanco ◽

I. Estacio ◽

L. R. Depositario

Keyword(s):

Surface Temperature ◽

Spatial Resolution ◽

Land Surface Temperature ◽

Water Consumption ◽

Land Surface ◽

Urban Climate ◽

Temporal Analysis ◽

Urban Heat Islands ◽

Landsat 8 ◽

The Relationship

Abstract. Land Surface Temperature (LST) is one of the important factors in monitoring urban climate. Observing the variations of LST can provide a better understanding of the Urban Heat Islands (UHI) phenomenon. The aim of this research is to assess the relationship between the spatial and temporal distribution of LST and water consumption in Zamboanga City for years 2016 and 2017. Data from the city’s water district were used to compute for the per capita water consumption (PCWC) of 49 barangays. Landsat 8 LST data with 30m spatial resolution were computed using inverse Plank function and other parameters such as vegetation proportion and surface emissivity to assess LST spatially while MODIS Terra data with 1km spatial resolution were used to assess LST temporally. Result showed that Landsat LST and PCWC have moderate correlations with p < 0.01: 0.59 and 0.55 for March and April 2016, respectively; 0.49 and 0.56 for March and April 2017, respectively. These indicated that warmer barangays consumed more water. The temporal correlation of the MODIS LST and the computed PCWC equated a −0.71, p < 0.01, correlation. This negative correlation indicated that when LST increases, PCWC decreases, which do not directly indicate that the city consumed less water but rather that the supply was less during warmer months. It was evident as water rationing was experienced during the first quarter of 2016 and intensified on April where the highest LST was recorded. Finally, LST was found of good use in assessing the relationship of temperature and water consumption.

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Exploring diurnal cycles of surface urban heat island intensity in Boston with land surface temperature data derived from GOES-R geostationary satellites

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.144224 ◽

2021 ◽

Vol 763 ◽

pp. 144224

Author(s):

Yue Chang ◽

Jingfeng Xiao ◽

Xuxiang Li ◽

Steve Frolking ◽

Decheng Zhou ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Temperature Data ◽

Heat Island ◽

Diurnal Cycles ◽

Geostationary Satellites ◽

Urban Heat ◽

Surface Urban Heat Island ◽

Surface Temperature Data

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Assessment of heat mitigation capacity of urban greenspaces with the use of InVEST urban cooling model, verified with day-time land surface temperature data

Landscape and Urban Planning ◽

10.1016/j.landurbplan.2021.104163 ◽

2021 ◽

Vol 214 ◽

pp. 104163

Author(s):

J.E. Zawadzka ◽

J.A. Harris ◽

R. Corstanje

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Temperature Data ◽

Urban Greenspaces ◽

Surface Temperature Data ◽

Cooling Model

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Estimation and Validation of Land Surface Temperature by using Remote Sensing & GIS for Chittoor District, Andhra Pradesh

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i5.1059 ◽

2021 ◽

Vol 12 (5) ◽

pp. 607-617

Author(s):

A. Rajani, Dr. S.Varadarajan

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Andhra Pradesh ◽

Temperature Data ◽

Sensor Data ◽

Spectral Radiance ◽

Landsat 8 ◽

Thermal Band

Land Surface Temperature (LST) quantification is needed in various applications like temporal analysis, identification of global warming, land use or land cover, water management, soil moisture estimation and natural disasters. The objective of this study is estimation as well as validation of temperature data at 14 Automatic Weather Stations (AWS) in Chittoor District of Andhra Pradesh with LST extracted by using remote sensing as well as Geographic Information System (GIS). Satellite data considered for estimation purpose is LANDSAT 8. Sensor data used for assessment of LST are OLI (Operational Land Imager) and TIR (Thermal Infrared). Thermal band contains spectral bands of 10 and 11 were considered for evaluating LST independently by using algorithm called Mono Window Algorithm (MWA). Land Surface Emissivity (LSE) is the vital parameter for calculating LST. The LSE estimation requires NDVI (Normalized Difference Vegetation Index) which is computed by using Band 4 (visible Red band) and band 5 (Near-Infra Red band) spectral radiance bands. Thermal band images having wavelength 11.2 µm and 12.5 µm of 30th May, 2015 and 21st October, 2015 were processed for the analysis of LST. Later on validation of estimated LST through in-suite temperature data obtained from 14 AWS stations in Chittoor district was carried out. The end results showed that, the LST retrieved by using proposed method achieved 5 per cent greater correlation coefficient (r) compared to LST retrieved by using existing method which is based on band 10.

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