Spatiotemporal analysis of land surface temperature using multi-temporal and multi-sensor image fusion techniques

2021 ◽  
Vol 64 ◽  
pp. 102508
Author(s):  
Keyvan Ezimand ◽  
Manouchehr Chahardoli ◽  
Mohsen Azadbakht ◽  
Ali Akbar Matkan
Keyword(s):  
Surface Temperature ◽  
Image Fusion ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatiotemporal Analysis ◽  
Multi Temporal

scholarly journals Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information

2018 ◽  
Vol 10 (7) ◽  
pp. 1112 ◽  
Author(s):  
Jian Kang ◽  
Junlei Tan ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Total Error ◽  
Predictor Variables ◽  
Multiple Time ◽  
Modeling Error ◽  
Multi Temporal ◽  
Reconstructed Data

Land surface temperature (LST) products derived from the moderate resolution imaging spectroradiometer (MODIS) sensor are one of the most important data sources used to research land surface energy and water balance at regional and global scales. However, MODIS data are severely contaminated by cloud cover, which limits the applications of LST products. In this paper, based on the spatio-temporal autocorrelation of land surface variables, a reconstruction algorithm depending on the correlations between spatial pixels in multiple time phases from available MODIS LST data is developed to reconstruct clear-sky LST values for missing pixels. Considering the impacts of correlation and bias between predictors and reconstructed data on the modeling error, the known data in the reconstructed time phase are combined with the data temporally nearest to them as predictor variables to establish their temporal relationships with the reconstructed data. The reconstructed results are validated by a series of evaluation indices. The average correlation coefficient between the reconstructed results and ground-based observations is 0.87, showing high temporal change accuracy. The difference in Moran’s I, representing spatial structure characteristics between the known and reconstructed data, is 0.03 on average, indicating a slight loss of spatial accuracy. The average reconstruction rate is approximately 87.0%. The modeling error, as part of the reconstruction error, is only 1.40 K on average and accounts for 5.0% of the total error. If the product and modeling errors are removed, the residual error represents approximately 3.5 K and 5.6 K of the annual mean difference between the cloudy and cloudless LST at night and during the day, respectively. In addition, different reconstruction cases are demonstrated using various predictor data, including many combinations of multi-temporal MODIS LST data, the microwave brightness temperature, and the combination of the normalized difference vegetation index and terrain data. Comparisons among cases show that the known MODIS LST data are more reliable as predictor variables and that the data combination advocated in this paper is optimal.

scholarly journals Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1540
Author(s):  
Zhengwu Cai ◽  
Chao Fan ◽  
Falin Chen ◽  
Xiaoma Li
Keyword(s):  
Linear Regression ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temperature Changes ◽  
Invariant Feature ◽  
Multi Temporal ◽  
Feature Based ◽  
Impact Of Urbanization ◽  
The Impact

The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. This study validated the utility of the pseudo-invariant feature-based linear regression model (PIF-LRM) in normalizing multi-temporal Landsat LST to highlight the urbanization impact on temperature changes, based on five Landsat LST images during 2000–2018 in Changsha, China. Results showed that LST of PIFs between the reference and the target images was highly correlated, indicating high applicability of the PIF-LRM to relatively normalize LST. The PIF-LRM effectively removed the temporal variation of LST caused by climate factors and highlighted the impacts of urbanization caused land use and land cover changes. The PIF-LRM normalized LST showed stronger correlations with the time series of normalized difference of vegetation index (NDVI) than the observed LST and the LST normalized by the commonly used mean method (subtracting LST by the average, respectively for each image). The PIF-LRM uncovered the spatially heterogeneous responses of LST to urban expansion. For example, LST decreased in the urban center (the already developed regions) and increased in the urbanizing regions. PIF-LRM is highly recommended to normalize multi-temporal Landsat LST to understand the impact of urbanization on surface temperature changes from a temporal point of view.

scholarly journals FUSION OF LANDSAT- 8 THERMAL INFRARED AND VISIBLE BANDS WITH MULTI-RESOLUTION ANALYSIS CONTOURLET METHODS

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 77-81 ◽  
Author(s):  
F. Farhanj ◽  
M. Akhoondzadeh
Keyword(s):  
Surface Temperature ◽  
Image Fusion ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Information ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

Land surface temperature image is an important product in many lithosphere and atmosphere applications. This image is retrieved from the thermal infrared bands. These bands have lower spatial resolution than the visible and near infrared data. Therefore, the details of temperature variation can't be clearly identified in land surface temperature images. The aim of this study is to enhance spatial information in thermal infrared bands. Image fusion is one of the efficient methods that are employed to enhance spatial resolution of the thermal bands by fusing these data with high spatial resolution visible bands. Multi-resolution analysis is an effective pixel level image fusion approach. In this paper, we use contourlet, non-subsampled contourlet and sharp frequency localization contourlet transform in fusion due to their advantages, high directionality and anisotropy. The absolute average difference and RMSE values show that with small distortion in the thermal content, the spatial information of the thermal infrared and the land surface temperature images is enhanced.

scholarly journals Land Use Change and Its Impacts on Land Surface Temperature in Galle City, Sri Lanka

Climate ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 65 ◽  
Author(s):  
DMSLB Dissanayake
Keyword(s):  
Land Use ◽  
Sri Lanka ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Land Use Land Cover ◽  
Multi Temporal ◽  
Negative Effect ◽  
The Mean ◽  
And Control

This study investigated the spatiotemporal changes of land use land cover (LULC) and its impact on land surface temperature (LST) in the Galle Municipal Council area (GMCA), Sri Lanka. The same was achieved by employing the multi-temporal satellite data and geo-spatial techniques between 1996 and 2019. The post-classification change detection technique was employed to determine the temporal changes of LULC, and its results were utilized to assess the LST variation over the LULC changes. The results revealed that the area had undergone a drastic LULC transformation. It experienced 38% increase in the built-up area, while vegetation and non-built-up area declined by 26% and 12%, respectively. Rapid urban growth has had a significant effect on the LST, and the built-up area had the highest mean LST of 22.7 °C, 23.2 °C, and 26.3 °C for 1996, 2009, and 2019, correspondingly. The mean LST of the GMCA was 19.2 °C in 1996, 20.1 °C in 2009, and 22.4 °C in 2019. The land area with a temperature above 24 °C increased by 9% and 12% in 2009 and 2019, respectively. The highest LST variation (5.5 °C) was observed from newly added built-up area, which was also transferred from vegetation land. Meanwhile, the lowest mean LST difference was observed from newly added vegetation land. The results show that the mean annual LST increased by 3.2 °C in the last 22 years in GMCA. This study identified significant challenges for urban planners and respective administrative bodies to mitigate and control the negative effect of LST for the long livability of Galle City.

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