Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area

Liu, Yuanbo; Hiyama, Tetsuya; Yamaguchi, Yasushi

doi:10.1016/j.rse.2006.06.012

Cited by 193 publications

(119 citation statements)

References 51 publications

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“…The mean root mean square difference between MODIS and ASTER data is 2.4 • C for all dates. This is consistent with recent studies reporting a discrepancy between ASTER and MODIS surface temperature of about 3 • C (Liu et al, 2006(Liu et al, , 2007. The same comparison is done at 2 km resolution by aggregating ASTER and 1 km re-sampled MODIS data.…”

Section: Modis Datasupporting

confidence: 91%

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

Merlin

Duchemin

Hagolle

et al. 2010

Remote Sensing of Environment

156

107

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The temporal frequency of the thermal data provided by current spaceborne high-resolution imagery systems is inadequate for agricultural applications.As an alternative to the lack of high-resolution observations, kilometric thermal data can be disaggregated using a green (photosynthetically active) veg- . The approach is also tested using the MODIS data re-sampled at 2 km resolution. Aggregation reduces errors in MODIS data and consequently increases the disaggregation accuracy.

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Section: Modis Datasupporting

confidence: 91%

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

Merlin

Duchemin

Hagolle

et al. 2010

Remote Sensing of Environment

156

107

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“…The low-resolution LST data was simulated by upscaling the LST image from the original 90-m resolution to 720-m resolution (Figure 2c). The upscaling was conducted by arithmetic averaging because other methods based on Stefan-Boltzmann law do not show obvious superiority [30]. Meanwhile, the NDVI image was also linearly resampled from 15-m to 90-m (Figure 2b).…”

Section: Simulation Experiments Using Aster Datamentioning

confidence: 99%

A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery

Chen

et al. 2014

Remote Sensing

Self Cite

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There have been many studies and much attention paid to spatial sharpening for thermal imagery. Among them, TsHARP, based on the good correlation between vegetation index and land surface temperature (LST), is regarded as a standard technique because of its operational simplicity and effectiveness. However, as LST is affected by other factors (e.g., soil moisture) in the areas with low vegetation cover, these areas cannot be well sharpened by TsHARP. Thin plate spline (TPS) is another popular downscaling technique for surface data. It has been shown to be accurate and robust for different datasets; however, it has not yet been attempted in thermal sharpening. This paper proposes to combine the TsHARP and TPS methods to enhance the advantages of each. The spatially explicit errors of these two methods were firstly estimated in theory, and then the results of TPS and TsHARP were combined with the estimation of their errors. The experiments performed across various landscapes and data showed that the proposed combined method performs more robustly and accurately than TsHARP.

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“…Note also that in this approach to re-scaling of thermal products it is important that spatial data considered for processing must represent relatively flat areas, thus the low scale can be expressed as a simple aerial average of the LSTs at the high scale [28]. Also, the re-scaling methods have limitations for areas with high surface type diversity, which particularly refers to wetlands, water containers situated among sub pixel areas.…”

Section: Pbim Methodsmentioning

confidence: 99%

Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk

Chybicki¹,

Łubniewski²

2017

Open Geosciences

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Satellite imaging systems have known limitations regarding their spatial and temporal resolution. The approaches based on subpixel mapping of the Earth's environment, which rely on combining the data retrieved from sensors of higher temporal and lower spatial resolution with the data characterized by lower temporal but higher spatial resolution, are of considerable interest. The paper presents the downscaling process of the land surface temperature (LST) derived from low resolution imagery acquired by the Advanced Very High Resolution Radiometer (AVHRR), using the inverse technique. The effective emissivity derived from another data source is used as a quantity describing thermal properties of the terrain in higher resolution, and allows the downsampling of low spatial resolution LST images. The authors propose an optimized downscaling method formulated as the inverse problem and show that the proposed approach yields better results than the use of other downsampling methods. The proposed method aims to find estimation of high spatial resolution LST data by minimizing the global error of the downscaling. In particular, for the investigated region of the Gulf of Gdańsk, the RMSE between the AVHRR image downscaled by the proposed method and the Landsat 8 LST reference image was 2.255 ∘ C with correlation coefficient R equal to 0.828 and Bias = 0.557 ∘ C. For comparison, using the PBIM method, it was obtained RMSE = 2.832 ∘ C, R = 0.775 and Bias = 0.997 ∘ C for the same satellite scene. It also has been shown that the obtained results are also good in local scale and can be used for areas much smaller than the entire satellite imagery scene, depicting diverse biophysical conditions. Specifically, for the analyzed set of small sub-datasets of the whole scene, the obtained RSME between the downscaled and reference image was smaller, by approx. 0.53 ∘ C on average, in the case of applying the proposed method than in the case of using the PBIM method.

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Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area

Cited by 193 publications

References 51 publications

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery

Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk

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