Disaggregation of modis land surface temperature in urban areas using improved thermal sharpening techniques

Bala, Ruchi; Prasad, Rajendra; Yadav, Vijay Pratap

doi:10.1016/j.asr.2019.05.004

Cited by 27 publications

(21 citation statements)

References 35 publications

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“…The land surface in urban areas is mainly composed of nonevaporation and nonpermeation materials, such as asphalt, concrete, stone, metal, etc. Normalized difference build-up index (NDBI) is an effective index for downscaling of LST in urban areas [18,34]. The NDBI image was derived as follows [18]:…”

Section: ) Mechanism Explanatory Variablesmentioning

confidence: 99%

“…Satellite-borne thermal infrared remote sensing images are the most important data sources for depicting LST at regional scales due to their advantage of providing spatially continuous and simultaneous views of an area [17,18]. However, most satellite-borne thermal infrared sensors produce images only in the daytime.…”

Section: Introductionmentioning

confidence: 99%

“…These methods are also called LST image sharpening, subpixel temperature estimation or disaggregation of LST in previous literature. These methods can be categorized into three groups [19]: statistical [18,19], physical [20,21] and spatiotemporal methods [22][23][24]. Among these groups, statistical methods have received more attention due to their accuracy and simplicity [1,19].…”

Section: Introductionmentioning

confidence: 99%

“…One trend of statistical LST downscaling research is the increase in the number of explanatory variables. Most early methods used a single explanatory variable, such as the normalized difference vegetation index (NDVI) [18,[25][26][27]. However, although those methods are effective in homogeneous agricultural areas, they may not perform well in highly heterogeneous areas, such as urban areas.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating Multivariable Statistical Methods for Downscaling Nighttime Land Surface Temperature in Urban Areas

Cui

Zhang

et al. 2020

IEEE Access

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For research and practice in fields such as the environment and meteorology, nighttime land surface temperature (LST) images at fine resolution provide important basic data. Since only a few satellite sensors can take fine-resolution thermal infrared images at night, such images are rather scarce. Downscaling of coarse-resolution LST images is a potential method for obtaining high-resolution LST images. However, downscaling methods have been mostly proposed for daytime LST images. This study aimed to evaluate the performance of methods that combine multiple explanatory variables and machine learning algorithms for downscaling coarse-resolution nighttime LST images in urban areas. Verification showed that the errors in the downscaling results were acceptable (mean absolute errors within 2 K). The resulting images could depict the spatial pattern of night LST in the study area in great detail. It was demonstrated that visible-near infrared images taken in the daytime could be used for downscaling of nighttime LST images, the rationality of which was deduced. It was also demonstrated that the performance of the spectral explanatory variables in nighttime LST downscaling was not lower than that of the mechanism explanatory variables. This method has high application value in many academic and practical efforts, such as land-atmosphere interface radiation budget studies, suitability assessments of human settlement, and urban environmental planning. INDEX TERMS Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER); downscaling; land surface temperature (LST); Landsat 8 Operational Land Imager (OLI); Moderate Resolution Imaging Spectroradiometer (MODIS); nighttime; urban areas.

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Section: ) Mechanism Explanatory Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating Multivariable Statistical Methods for Downscaling Nighttime Land Surface Temperature in Urban Areas

Cui

Zhang

et al. 2020

IEEE Access

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“…UHI formation depends on various factors like the type of natural land cover surrounding the urban area or the time of the study (Chen et al 2006, Bala et al 2018. Various studies have been performed to study the relation of LST with different independent variables like Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), percent impervious surfaces (Li et al 2018, Bala et al 2019, Yuan and Bauer, 2007. Moderate Resolution Imaging Spectro-radiometer (MODIS) data provides LST images for day as well as night time.…”

Section: Introductionmentioning

confidence: 99%

Spatial Variation of Urban Heat Island Intensity in Urban Cities Using Modis Satellite Data

Bala

Prasad

Yadav

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Urban Heat Island (UHI) refers to the occurrence of higher temperatures in urban areas than the neighbouring rural areas. The neighbouring land cover also has some influence on the urban temperatures. The present study focuses on the UHI effect observed in four different cities i.e. Bikaner, Hyderabad, Vadodara and Varanasi which were surrounded by different natural land covers using MODIS satellite images. Bikaner shows Urban Cool Island (UCI) and Varanasi show UHI during day time. Vadodara and Hyderabad do not show much variation in urban and rural LST during day time. However, UHI effect was found clearly significant during night time in the four cities. UHI intensity was calculated using night LST and found highest in Hyderabad and lowest in Bikaner. The relation of LST with % Impervious was studied which shows good positive linear relation when significant UHI effect was observed and negative linear relation when UCI effect was observed. The slope obtained from linear regression of night LST with % Impervious was compared with the UHI intensity in the four cities and found to show good positive linear relation. Therefore, % Impervious can be used to quantify UHI intensity in urban areas.

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Downscaling SMAP soil moisture product in cold and arid region: Incorporating NDSI and BSI into the random forest algorithm

Gao,

Zhu,

Guo

et al. 2024

Vadose Zone Journal

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Soil moisture (SM) is a critical element of the hydrological cycle, land surface processes, and surface energy balance. However, the low spatial resolution of commonly used SM products limits the application of SM in agriculture and eco‐hydrology in cold and arid regions. In this study, the normalized difference soil index (NDSI) and bare soil index (BSI) were added to traditional downscaling factors including land surface temperature, normalized difference vegetation index, digital elevation mode, apparent thermal inertia, Albedo, and temperature vegetation dryness index, as they are more strongly correlated with surface SM in the bare soil‐vegetation alternation zone of such region. Using the random forest algorithm, a downscaling model of SM was constructed for such region. The accuracy of the downscaled SM estimates was validated by comparing them with the original SM data collected from May to September 2021, which is the non‐freezing period of the soil. The findings indicate that the newly added NDSI and BSI have good correlation with SM. Incorporating NDSI and BSI to construct the downscaled model enhances the accuracy by over 19% compared to excluding them, while also providing a more comprehensive representation of SM information. NDSI and BSI can be well applied to the downscaled research of SM in the bare soil‐vegetation alternation zone, which is of great value for the study of eco‐hydrology and agricultural drought monitoring in cold and arid regions.

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Disaggregation of modis land surface temperature in urban areas using improved thermal sharpening techniques

Cited by 27 publications

References 35 publications

Evaluating Multivariable Statistical Methods for Downscaling Nighttime Land Surface Temperature in Urban Areas

Evaluating Multivariable Statistical Methods for Downscaling Nighttime Land Surface Temperature in Urban Areas

Spatial Variation of Urban Heat Island Intensity in Urban Cities Using Modis Satellite Data

Downscaling SMAP soil moisture product in cold and arid region: Incorporating NDSI and BSI into the random forest algorithm

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