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Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scaleinvariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990m ASTER TIR data to 90m resolution. When validated against the original 90m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods.
Satellite thermal infrared (TIR) imagery is the primary source to retrieve land surface temperature (LST) for various ecological and environmental studies at regional and global scales [
Two basic approaches have been developed to enhance the LST products retrieved from satellite TIR imagery. The first approach uses spectral mixture analysis (SMA) to decompose mixed TIR pixels into multiple isothermal components [
In this paper, we present a downscaling approach for enhancing LST products using satellite TIR imagery. Given the fact that the algorithms for retrieving LST from TIR radiance are well established [
The basic idea of the physical downscaling method is to establish a functional relationship between TIR radiance and ancillary data, which satisfies two conditions: (1) the functional relationship is physically meaningful and holds across different scales; and (2) the ancillary data can be easily scaled. Specifically, we modeled the TIR radiance as a linear combination of multiple land cover components weighted by their corresponding fractions and modified by atmospheric effects.
Under constant atmospheric conditions, the atsensor TIR radiance
Spectral mixture analysis attempts to retrieve
Similarly, the isothermal assumption indicates that
Using the functional relationship obtained at the coarse spatial resolution, the downscaled atsensor radiance
A statistical approach to downscaling TIR radiance directly estimates the subpixel TIR radiance
The TIR radiance used to test our downscaling methods is from Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER). ASTER is one of the major satellite sensors used for land surface temperature retrieval [
As the five ASTER thermal channels are highly correlated with each other, we only selected the thermal infrared data in channel 13 (10.25–10.95μm) in this study. The ASTER Level1B data set contains radiometrically calibrated digital numbers (DNs) quantized as 16bit unsigned integers for the TIR bands. We converted the scaled DNs in channel 13 to atsensor TIR radiances by the formula [
To evaluate the proposed downscaling methods, multiresolution TIR radiances in a particular spectral region acquired at the same time and location are needed for validation. We simulated multiresolution thermal data by upscaling the ASTER channel 13 from the original 90m resolution to 990m resolution. This allows us to apply the proposed methods to downscale the 990m thermal data back to 90m resolution, which will then be validated against the original 90m thermal data. Specifically, the TIR radiances were upscaled by averaging every 11 × 11 TIR pixels at the 90m resolution to one pixel at the 990m resolution [
The three 15m ASTER VNIR channels shown in
The physical model between ASTER atsensor TIR radiances and land cover fractions at the resolution of 990m was well fitted to the data as indicated by a very high coefficient of determination
When the model parameter estimates obtained at the resolution of 990m were applied to land cover fractions at the spatial resolution of 90m, the 90m TIR radiances were obtained. After bias correction (see
The results of the 90m TIR radiances by statistical downscaling were shown in
Subpixel LST retrieval is often needed in various environmental and ecological studies because the LST products retrieved from current satellite thermal sensors have limited spatial resolutions for finerscale studies and may contain mixed pixels of multiple anisothermal objects in heterogeneous areas. In this paper, we developed two methods for downscaling coarse resolution TIR radiances in preparing for subsequent subpixel temperature retrieval. The first method was developed on the basis of a scaleinvariant physical model on TIR radiances. From the physical model, a functional relationship between TIR radiances and land cover fractions was estimated using data at a coarse spatial resolution. The downscaled TIR radiances were then estimated by applying this functional relationship to land cover fractions at a high spatial resolution. The second method was based on a statistical relationship between TIR radiances and land cover fractions at a high spatial resolution. In this statistical downscaling method, high spatial resolution TIR radiances were initialized by the coarse spatial resolution observations and then iteratively regressed to the high spatial resolution land cover fractions until no significant improvements between two continuous iterations were achieved.
The two downscaling methods were applied to simulated 990m TIR radiances of ASTER channel 13. The estimated 90m TIR radiances were then validated against the original 90m TIR radiances. The visual comparison of the results revealed that both downscaling methods successfully captured the general patterns of the original data and resolved considerable spatial details. The quantitative assessments indicated a strong agreement between the true values and the estimated values generated by both methods. Future research could consider the use of spatial dependence in the downscaling methods and explore other ancillary data. In conclusion, the downscaling methods developed in this paper showed promising results for further subpixel land surface temperature retrieval.
Procedure of statistical downscaling method.
The false color display of the 15m ASTER VNIR imagery in the study area.
(a) The original 90m TIR radiances of ASTER channel 13, (b) The simulated 990m TIR radiances of ASTER channel 13, (c) The estimated 90m TIR radiances by physical downscaling, and (d) The estimated 90m TIR radiances by statistical downscaling. Unit for the scale bar is w / (m^{2} sr μm).
Estimated 90m TIR radiances of ASTER channel 13 by physical downscaling. Solid lines are the fitted models and dotted lines are 1:1 lines. Units for both axis are w / (m^{2} sr μm).
Estimated 90m TIR radiances of ASTER channel 13 by statistical downscaling. Solid lines are the fitted models and dotted lines are 1:1 lines. Units for both axis are w / (m^{2} sr μm).
The parameter estimates of the physical model.
Parameters  Estimate  Standard Error  Pvalue 


2.56986  0.18414  <2e16 

0.49519  0.03397  <2e16 

0.70226  0.01831  <2e16 

0.71396  0.01829  <2e16 

0.74050  0.02203  <2e16 

0.69724  0.01876  <2e16 

0.63358  0.02272  <2e16 

0.64320  0.02016  <2e16 