Downscaling Land Surface Temperature in an Urban Area: A Case Study for Hamburg, Germany

Bechtel, Benjamin; Zakšek, Klemen; Hoshyaripour, Gholam Ali

doi:10.3390/rs4103184

Cited by 144 publications

(109 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The scatter distribution is comparable to other studies using different sensors in different environments [34,36,42]. Same findings for seasonal variation in dependence of LST on the NDVI were also found by Bechtel (2012) [89].…”

Section: Class-specific Dependenciessupporting

confidence: 77%

See 1 more Smart Citation

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Wicki

Parlow

2017

Remote Sensing

View full text Add to dashboard Cite

Global climate change and increasing urbanization worldwide intensify the need for a better understanding of human heat stress dynamics in urban systems. During heat waves, which are expected to increase in number and intensity, the development of urban cool islands could be a lifesaver for many elderly and vulnerable people. The use of remote sensing data offers the unique possibility to study these dynamics with spatially distributed large datasets during all seasons of the year and including day and night-time analysis. For the city of Basel 32 high-quality Landsat 8 (L8) scenes are available since 2013, enabling comprehensive statistical analysis. Therefore, land surface temperature (LST) is calculated using L8 thermal infrared (TIR) imagery (stray light corrected) applying improved emissivity and atmospheric corrections. The data are combined with a land use/land cover (LULC) map and evaluated using administrative residential units. The observed dependence of LST on LULC is analyzed using a thermal unmixing approach based on a multiple linear regression (MLR) model, which allows for quantifying the gradual influence of different LULC types on the LST precisely. Seasonal variations due to different solar irradiance and vegetation cover indicate a higher dependence of LST on the LULC during the warmer summer months and an increasing influence of the topography and albedo during the colder seasons. Furthermore, the MLR analysis allows creating predicted LST images, which can be used to fill data gaps like in SLC-off Landsat 7 ETM+ data.

show abstract

Section: Class-specific Dependenciessupporting

confidence: 77%

“…Values of up to 0.87 for R 2 are comparable to other studies, e.g., Bechtel (2011) or Bechtel (2012) [35,89], and overall p-values below the 5% significance benchmark for all model runs indicate high reliability. The resulting coefficients show comparable tendencies within different summer scenes.…”

Section: Multiple Linear Regression Analysissupporting

confidence: 65%

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Wicki

Parlow

2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Due to their very high spatial resolution, the thermal band data of the Landsat series, such as the Landsat 5 Thematic Mapper (TM) and the Landsat 7 Enhanced Thematic Mapper (ETM), have been widely applied for LST retrieval for such studies as surface energy budget estimation [3,4], surface moisture and evapotranspiration monitoring [5,6], urban heat island monitoring [7,8] and environmental biogeochemistry process simulation [9], requiring LST as a basic input.…”

Section: Introductionmentioning

confidence: 99%

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

et al. 2015

View full text Add to dashboard Cite

Abstract:The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for OPEN ACCESSRemote Sens. 2015, 7 4269 the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.

show abstract

“…Large numbers of variables accounting for different sources of influence on Ta usually mean (high) correlations among them [68,73]. Yet, previous regression-based Ta studies have not considered the collinearity among predictors an important issue primarily because the Ta estimation has mainly been conducted within the range of sampled data, i.e., when the influence of collinearity is limited [45].…”

Section: Nighttime Light Predictor Independence and Coefficient Dynmentioning

confidence: 99%

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

et al. 2016

View full text Add to dashboard Cite

Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001-2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R 2 ) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18-2.60 K in RMSE and 0.00-0.15 in R 2 ).

show abstract

Downscaling Land Surface Temperature in an Urban Area: A Case Study for Hamburg, Germany

Cited by 144 publications

References 55 publications

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

Contact Info

Product

Resources

About