Step-By-Step Downscaling of Land Surface Temperature Considering Urban Spatial Morphological Parameters
Abstract
:1. Introduction
2. Research Review
3. Materials and Methods
3.1. Study Area
3.2. Data
3.2.1. Landsat LST Product
3.2.2. Sentinel-3 LST Product
3.2.3. Sentinel-2 Multispectral Data
3.2.4. Urban Building Data
3.3. Calculation of the Downscaling Driving Factor
3.3.1. Calculation of the Remote-Sensing Spectral Index
3.3.2. Calculation of Urban Spatial Morphological Parameters
3.4. Downscaling LST Method Based on Random Forest
3.5. Step-By-Step Random Forest Downscaling Method (SSRFD)
3.6. Accuracy Evaluation Methods
4. Results
4.1. Comparison of the Results Obtained with SSRFD and DRFD
4.2. Influence of Urban Spatial Morphological Parameters on Downscaling LST
4.2.1. Analysis of the Overall Downscaling Results in the Study Area
4.2.2. Analysis of Regional Downscaling Results in the Study Area
4.3. Parameter Importance Analysis of LST Downscaling
5. Discussion
6. Conclusions
- (1)
- The 900-m LST was downscaled step-by-step on the order of 450, 150 and 30 m. Compared to the results obtained with DRFD, the r value between the SSRFD results and Landsat LST was improved by 0.21, and the RMSE value was reduced by 0.94 °C. The SSRFD results more accurately captured the spatial distribution characteristics of the surface temperature, including the high-temperature zone of buildings and the low-temperature zone of water and vegetation. The underestimation/overestimation phenomenon of DRFD resulting in large errors in the high/low temperature zone was avoided or attenuated when using the SSRFD method.
- (2)
- The results obtained with SSRFD-M were partially significantly improved in the Gulou, Qinhuai and Jianye built-up areas compared to SSRFD, in which r and RMSE values improved/decreased by approximately 0.15 and 0.46 °C, respectively. The phenomenon of low-temperature zones in vegetation-covered areas when only remote-sensing spectral indices were used was improved. The SSRFD-M results to some extent compensated for the deficiency of remote-sensing spectral indices used for urban LST downscaling.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Var. | Description | Equations |
---|---|---|
MNDWI | Improve the normalized difference water body index to highlight water body information | |
NDBI | Normalize the difference building index to highlight building information | |
NDBSI | Indicate the degree of dryness of the ground surface [45] | |
NDMI | Indicate the vegetation moisture | |
NDVI | Highlight vegetation information | |
SAVI | Reduce the sensitivity of vegetation indices to changes in reflectance of different soils | L = 0.5 |
Var. | Description | Equations |
---|---|---|
BD | Building Density | Ai indicates the ith building area and AT indicates the calculated plot area |
FAD | Frontal Area Density | indicates the frequency of the wind direction θ, i = 1,…, 16 |
FAR | Floor Area Ratio | the ith building area |
MH | Mean Height | indicates the ith building height and n indicates the number of buildings |
SVF | Sky View Factor | ψsky indicates SVF, β indicates the building height angle, H indicates the building height, X indicates the calculated radius and is set to 100 m in this study |
Statistical Variables | Reference LST/°C | DRFD LST/°C | SSRFD LST/°C |
---|---|---|---|
Maximum | 51.06 | 42.80 | 50.76 |
Minimum | 13.94 | 28.95 | 24.36 |
Mean | 36.50 | 37.91 | 36.78 |
Standard deviation | 3.46 | 2.64 | 3.73 |
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Li, X.; Zhang, G.; Zhu, S.; Xu, Y. Step-By-Step Downscaling of Land Surface Temperature Considering Urban Spatial Morphological Parameters. Remote Sens. 2022, 14, 3038. https://doi.org/10.3390/rs14133038
Li X, Zhang G, Zhu S, Xu Y. Step-By-Step Downscaling of Land Surface Temperature Considering Urban Spatial Morphological Parameters. Remote Sensing. 2022; 14(13):3038. https://doi.org/10.3390/rs14133038
Chicago/Turabian StyleLi, Xiangyu, Guixin Zhang, Shanyou Zhu, and Yongming Xu. 2022. "Step-By-Step Downscaling of Land Surface Temperature Considering Urban Spatial Morphological Parameters" Remote Sensing 14, no. 13: 3038. https://doi.org/10.3390/rs14133038
APA StyleLi, X., Zhang, G., Zhu, S., & Xu, Y. (2022). Step-By-Step Downscaling of Land Surface Temperature Considering Urban Spatial Morphological Parameters. Remote Sensing, 14(13), 3038. https://doi.org/10.3390/rs14133038