Generating the 30-m land surface temperature product over continental China and USA from landsat 5/7/8 data

Cheng, Jie; Meng, Xianghai; Dong, Shengyue; Liang, Shunlin

doi:10.1016/j.srs.2021.100032

Cited by 32 publications

(13 citation statements)

References 95 publications

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“…We choose the image which was taken on 9 October 2020 at 02:01:30 (Greenwich Mean Time), and the detailed information is shown in Table 1. To ensure the accuracy of the result of land surface temperature retrieving, we use the retrieving algorithm proposed by Cheng et al [32]. This algorithm can calculate 30 m land surface temperature, which has better accuracy than USGS land surface temperature product [32].…”

Section: Landsat Datamentioning

confidence: 99%

“…To ensure the accuracy of the result of land surface temperature retrieving, we use the retrieving algorithm proposed by Cheng et al [32]. This algorithm can calculate 30 m land surface temperature, which has better accuracy than USGS land surface temperature product [32]. After atmosphere correction, the algorithm can be divided into two steps.…”

Section: Landsat Datamentioning

confidence: 99%

“…For non-vegetated surfaces, calculate the land surface emissivity (LSE) in different land cover types. For nonvegetated surfaces, Cheng et al [32] established the empirical equation of land surface emissivity and Landsat SRs.…”

Section: Landsat Datamentioning

confidence: 99%

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Quantifying the Scale Effect of the Relationship between Land Surface Temperature and Landscape Pattern

Chen

Wang

et al. 2023

Remote Sensing

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The spatial scaling of patterns and processes is a hot topic of research in landscape ecology, and different scales may yield completely inconsistent results. Therefore, to understand the impact of the scale effect on urban heat island effect, this study analyzes the correlation between surface temperature and landscape index at different spatial scales over Nanjing. The scale effect is calculated thorough curve fitting of the Pearson’s correlation coefficient between ten landscape indices and land surface temperature at different window sizes, and the optimal one is determined. We have found that landscape indices can be divided into exponential and Gaussian landscape indices whose correlation with land surface temperature at different windows conforms to binomial exponential or multi-Gaussian functions, respectively. The optimal window size is approximately 4000–5100 m for exponential landscape indices, 1000–2000 m for aggregation index (AI) and percentage of like adjacencies (PLADJ), 6330 m for contagion (CONTAG) and 4380 m for total edge contrast index (TECI). Moreover, CONTAG and TECI have a high correlation coefficient plateau where the Pearson correlation coefficient is high and changes by less than 0.03 as the window size changes by more than 3000 m, which makes it possible to decrease the window size in order to save the calculation time without an obvious decrease in the Pearson correlation coefficient. To achieve this, we proposed a suitable window selection function so that the window size becomes 4260 m and 2070 m, respectively. The window sizes obtained in this study are just suitable in Nanjing, but the window sizes in other cities can also be obtained by the method in this study. This study provides a reference for future research on the relationship between landscape pattern and land surface temperature and its driving mechanisms, as well as for the impact of urban land use planning on the heat island effect.

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Section: Landsat Datamentioning

confidence: 99%

Section: Landsat Datamentioning

confidence: 99%

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Quantifying the Scale Effect of the Relationship between Land Surface Temperature and Landscape Pattern

Chen

Wang

et al. 2023

Remote Sensing

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“…In general, the differences of LST from different sensors are not only affected by atmospheric conditions, performance and posture of sensors and observation angel [17], which are same as surface reflectance data, but also the transit time of different sensors and the inversion accuracy of LST. For example, the LST image retrieved from MODIS image via a generalized split-window algorithm which errors can reach to 1K under ideal conditions [67], [68], [69], [70], and the retrieved LST data from Landsat image is based on single channel algorithm which the errors will be about 1.5K under strict condition control [71], [72], and a recent research finds the retrieved errors from Landsat 5/7/8 LST products can reach 2-3K [73]. Furthermore, the time difference between the LST retrieved from different satellite images is about 30 minutes.…”

Section: B Cfsdafmentioning

confidence: 99%

“…Although the predicted increments of fine resolution LST by equation ( 6)-( 7) can be regarded as the optimal results, they are not equal to real increments [73]. It has some residuals between predicted increments and real increments, and the residuals can be expressed as:…”

Section: ) (4) Get the Spatial Increments By Idw Interpolationmentioning

confidence: 99%

A Comprehensive Flexible Spatiotemporal DAta Fusion Method (CFSDAF) for Generating High Spatiotemporal Resolution Land Surface Temperature in Urban Area

Shi

Wang

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Spatiotemporal fusion of land surface temperature (LST) has a vital significance in studying the temporal and spatial variation of urban heat islands. But most existing LST fusion methods don't consider the high heterogenous urban surface and complexity of spatial layout. In this study, a Comprehensive Flexible Spatiotemporal DAta Fusion (CFSDAF) method was proposed to generate high spatiotemporal resolution urban LST image which was improvements of the Flexible Spatiotemporal DAta Fusion (FSDAF). The CFSDAF first adjusted the differences between coarse resolution LST and fine resolution LST. Then, the visible and near-infrared image of fine resolution was introduced to execute spectral unmixing and to conduct soft classification, which considered the mixed pixel of fine resolution LST. The inverse distance weighting (IDW) interpolation was used in improving the computational efficiency, and the constrained least squares (CLS) was selected to better distribute the residual. The performance of CFSDAF was compared with the temporal adaptive reflectance fusion model (STARFM) and FSDAF. The results indicate that the predicted images by CFSDAF are better than STARFM and FSDAF from both visual comparison and quantitative assessment in two experiments, and CFSDAF can reserve more spatial details and accurately restore the spatial continuity of urban LST than others. Moreover, CFSDAF has high computational efficiency, and can monitor land cover changes same as FSDAF. Due to the above advantages, the CFSDAF has great potential for studying spatiotemporal changes of LST and UHI in urban area.

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Quantitative analysis of lake-cooling effect in Hefei City, China, based on multispectral remote sensing and its response to urban expansion

Lin

Cui

Hao

et al. 2022

Environ Sci Pollut Res

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Generating the 30-m land surface temperature product over continental China and USA from landsat 5/7/8 data

Cited by 32 publications

References 95 publications

Quantifying the Scale Effect of the Relationship between Land Surface Temperature and Landscape Pattern

Quantifying the Scale Effect of the Relationship between Land Surface Temperature and Landscape Pattern

A Comprehensive Flexible Spatiotemporal DAta Fusion Method (CFSDAF) for Generating High Spatiotemporal Resolution Land Surface Temperature in Urban Area

Quantitative analysis of lake-cooling effect in Hefei City, China, based on multispectral remote sensing and its response to urban expansion

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