Spatio-temporal variability in remotely sensed land surface temperature, and its relationship with physiographic variables in the Russian Altay Mountains

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

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Spatio-temporal analysis and simulation of land cover changes and their impacts on land surface temperature in urban agglomeration of Bisha Watershed, Saudi Arabia

Geocarto International ◽

10.1080/10106049.2021.1980616 ◽

2021 ◽

pp. 1-31

Author(s):

Javed Mallick ◽

Vijay P. Singh ◽

Mohammed K. Almesfer ◽

Swapan Talukdar ◽

Majed Alsubhi ◽

...

Keyword(s):

Saudi Arabia ◽

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Temporal Analysis ◽

Urban Agglomeration ◽

Land Cover Changes ◽

Spatio Temporal

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Clarifying the role of radiative mechanisms in the spatio-temporal changes of land surface temperature across the Horn of Africa

Remote Sensing of Environment ◽

10.1016/j.rse.2018.11.024 ◽

2019 ◽

Vol 221 ◽

pp. 210-224 ◽

Cited By ~ 11

Author(s):

Temesgen Alemayehu Abera ◽

Janne Heiskanen ◽

Petri Pellikka ◽

Miina Rautiainen ◽

Eduardo Eiji Maeda

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Temporal Changes ◽

Horn Of Africa ◽

Spatio Temporal

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Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7070275 ◽

2018 ◽

Vol 7 (7) ◽

pp. 275 ◽

Cited By ~ 1

Author(s):

Bipin Acharya ◽

Chunxiang Cao ◽

Min Xu ◽

Laxman Khanal ◽

Shahid Naeem ◽

...

Keyword(s):

Surface Temperature ◽

Dengue Fever ◽

Land Surface Temperature ◽

Land Surface ◽

Environmental Variables ◽

Vegetation Index ◽

Generalized Additive Model ◽

Additive Model ◽

Remotely Sensed ◽

Cross Correlation Analysis

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

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