Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs

Chatterjee, R. S.; Singh, Narendra; Thapa, Shailaja; Sharma, Dravneeta; Kumar, Devendra

doi:10.1016/j.jag.2017.02.017

Cited by 60 publications

(25 citation statements)

References 38 publications

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“…Differing from the MWA, the SC method does not need the effective mean atmospheric temperature (Ta) parameter, and the atmospheric water vapor content is not required to be calculated to the atmospheric transmittance which reduces the error of the final retrieved LST due to the error of the effective mean atmospheric temperature (Ta). For these advantages, after the single-channel algorithm was proposed, it was used by many scholars for various types of thermal infrared remote sensing, such as Landsat 5 TM, Landsat 7 ETM+, MODIS, ASTER, and ENVISAT AATSR [19,20,37,38]. In 2014, Jiménez-Muñoz and Sobrino [15] improved the SC method for Landsat 8 and calculated the corresponding parameters, the formulas of the SC method are shown as follow:Ts=sans-serifγ[1ε(Ψ1Lsen+Ψ2)+Ψ3]+sans-serifδ where Ts is the LST; ε is LSE; γ and δ are two parameters depending on the Planck function and can be calculated by Formulas (21) and (22); in the formulas, Lsen is at-sensor registered radiance (W/(m 2 ·sr·μm)); Tsen is the at-sensor brigntness tempe...…”

Section: Methodsmentioning

confidence: 99%

“…In addition, the MWA needs one more parameter, the effective mean atmospheric temperature (Ta). In the acquisition of these parameters, scholars use many different methods [18,19,20,21]. Taking the acquisition of atmospheric transmittance as an example, some scholars calculate the atmospheric transmittance from water vapor content [10], some obtain it from the USGS website based on the time of image acquisition and the central latitude and longitude of image [22].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images

Wang

Yao

2019

Sensors

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The successful launch of the Landsat 8 satellite provides important data for the monitoring of urban heat island effects. Since the Landsat 8 TIRS data has two thermal infrared bands, it is suitable for many algorithms to retrieve the land surface temperature (LST). However, the selection of algorithms for retrieving the LST, the acquisition of algorithm input parameters, and the verification of the results are problems without obvious solutions. Taking Changchun City as an example, this paper used the mono-window algorithm (MWA), the split window algorithm (SWA), and the single-channel (SC) method to extract the LST from the Landsat 8 image and compared the three algorithms in terms of input parameters, accuracy, and sensitivity. The results show that all three algorithms can achieve good results in retrieving the LST. The SWA is the least sensitive to the error of the input parameters. The MWA and the SC method are sensitive to the error of the input parameters, and compared with the error of the LSE, these two algorithms are more sensitive to the error of atmospheric water vapor content. In addition, the MWA is also very sensitive to the error of the effective mean atmospheric temperature.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images

Wang

Yao

2019

Sensors

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“…Land radiative temperature, more commonly known as Land Surface Temperature (LST) [1,2], is a key Earth surface parameter on regional and global scales [3][4][5]. Accompanied by drastic land-use change and rapid urbanization, the transformation from natural landscapes to built-up urban areas has led to changes in land surface physics, as a consequence [6].…”

Section: Introductionmentioning

confidence: 99%

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

et al. 2019

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Land surface temperature (LST) is a fundamental Earth parameter, on both regional and global scales. We used seven Landsat images to derive LST at Suzhou City, in spring and summer 1996, 2004, and 2016, and examined the spatial factors that influence the LST patterns. Candidate spatial factors include (1) land coverage indices, such as the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), (2) proximity factors such as the distances to the city center, town centers, and major roads, and (3) the LST location. Our results showed that the intensity of the surface urban heat island (SUHI) has continuously increased, over time, and the spatial distribution of SUHI was different between the two seasons. The SUHIs in Suzhou were mainly distributed in the city center, in 1996, but expanded to near suburban, in 2004 and 2016, with a substantial expansion at the highest level of SUHIs. Our buffer-zone-based gradient analysis showed that the LST decays logarithmically, or decreases linearly, with the distance to the Suzhou city center. As inferred by the generalized additive models (GAMs), strong relationships exist between the LST and the candidate factors, where the dominant factor was NDBI, followed by NDWI and NDVI. While the land coverage indices were the LST dominant factors, the spatial proximity and location also substantially influenced the LST and the SUHIs. This work improved our understanding of the SUHIs and their impacts in Suzhou, and should be helpful for policymakers to formulate counter-measures for mitigating SUHI effects.

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“…Based on the physical linkage between LST and Ta, several authors have offered methods to estimate Ta using remote sensing satellite data [5,18,19]. During past decades, a large body of research has been collected regarding the retrieval of LST from satellite-based thermal infrared (TIR) data [20], in particular that related to the better understanding of emissivity and atmospheric effects [18,21,22]. As a result, LST can be retrieved nowadays relatively accurately from remotely sensed TIR data [23].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques

et al. 2019

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The objective of this research was to develop a robust statistical model to estimate climatologies (2002–2017) of monthly average near-surface air temperature (Ta) over Mongolia using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) time series products and terrain parameters. Two regression models were analyzed in this study linking automatic weather station data (Ta) with Earth observation (EO) images: partial least squares (PLS) and random forest (RF). Both models were trained to predict Ta climatologies for each of the twelve months, using up to 17 variables as predictors. The models were applied to the entire land surface of Mongolia, the eighteenth largest but most sparsely populated country in the world. Twelve of the predictor variables were derived from the LST time series products of the Terra MODIS satellite. The LST MOD11A2 (collection 6) products provided thermal information at a spatial resolution of 1 km and with 8-day temporal resolution from 2002 to 2017. Three terrain variables, namely, elevation, slope, and aspect, were extracted using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and two variables describing the geographical location of weather stations were extracted from vector data. For training, a total of 8544 meteorological data points from 63 automatic weather stations were used covering the same period as MODIS LST products. The PLS regression resulted in a coefficient of determination (R2) between 0.74 and 0.87 and a root-mean-square error (RMSE) from 1.20 °C to 2.19 °C between measured and estimated monthly Ta. The non-linear RF regression yielded even more accurate results with R2 in the range from 0.82 to 0.95 and RMSE from 0.84 °C to 1.93 °C. Using RF, the two best modeled months were July and August and the two worst months were January and February. The four most predictive variables were day/nighttime LST, elevation, and latitude. Using the developed RF models, spatial maps of the monthly average Ta at a spatial resolution of 1 km were generated for Mongolia (~1566 × 106 km2). This spatial dataset might be useful for various environmental applications. The method is transparent and relatively easy to implement.

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Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs

Cited by 60 publications

References 38 publications

Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images

Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques

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