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Human-induced global warming has significantly increased the importance of satellite monitoring of land surface temperature (LST) on a global scale. The MODerate-resolution Imaging Spectroradiometer (MODIS) provides a 1-km resolution LST product with almost daily coverage of the Earth, invaluable to both local and global change studies. The Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) provides a LST product with a high spatial resolution of 90-m and a 16-day recurrent cycle, simultaneously acquired at the same height and nadir view as MODIS. ASTER and MODIS are complementary in resolution, offering a unique opportunity for scale-related studies. ASTER and MODIS LST have been widely used but the errors in LST were mostly disregarded. Correction of ASTER-to-MODIS LST discrepancies is essential for studies reliant upon the joint use of these sensors. In this study, we compared three correction approaches: the Wan et al.'s approach, the refined Wan et al.'s approach, and the generalized split window (GSW) algorithm based approach. The Wan et al.'s approach corrects the MODIS 1-km LST using MODIS 5-km LST. The refined approach modifies the Wan et al.'s approach through incorporating ASTER emissivity and MODIS 5-km data. The GSW algorithm approach does not use MODIS 5-km but only ASTER emissivity data. We examined the case over a semi-arid terrain area for the part of the Loess Plateau of China. All the approaches reduced the ASTER-to-MODIS LST discrepancy effectively. With terrain correction, the original ASTER-to-MODIS LST difference reduced from 2.7±1.28 K to -0.1±1.87 K for the Wan et al.'s approach, 0.2±1.57 K for the refined approach, and 0.1±1.33 K for the GSW algorithm based approach. Among all the approaches, the GSW algorithm based approach performed best in terms of mean, standard deviation, root mean square root, and correlation coefficient.

While human-induced global warming very likely will continue, there is a scientific consensus for monitoring environmental for comprehensive understanding on a global scale [

The MODerate-resolution Imaging Spectroradiometer (MODIS) is one of five scientific instruments onboard the satellite platform, Terra, part of NASA's Earth Observation System (EOS), and provides data for retrieving land surface temperature (LST) at 1-km resolution with almost daily coverage of the Earth, which is invaluable for both local and global change research [

As a result of being onboard the same satellite platform, ASTER and MODIS are complementary in spatial and temporal resolutions, offering a unique opportunity for scale-related studies. In general, the remotely sensed pixel-wise value reflects a mixture of different land covers (spatial heterogeneity). The simultaneous observations made at the same height and coincident nadirs eliminate the differences in atmospheric and surface conditions. The observations at different spatial resolutions are important for clarifying the effects of spatial heterogeneity. For example, ASTER and MODIS LST data have been used to investigate scale influences on the retrieval of evaporation [

Recent studies reported the discrepancy in ASTER and MODIS LST of approximately 3 K over a semi-arid area [

It was found that the limited accuracy of LST retrieval algorithm was the major uncertainty source contributing to the ASTER-to-MODIS LST discrepancy [

In this study, we compare the Wan et al.'s approach, the refined Wan et al.'s approach, and the GSW algorithm based approach to reduce ASTER-MODIS LST discrepancy. Section 2 introduces three correction approaches. Section 3 describes the ASTER and MODIS data used and data processing. Section 4 inter-compares the results from three approaches. We also discuss the advantages and disadvantages of each approach for practical use.

Wan and Dozier developed the generalized split-window (GSW) algorithm for retrieval of 1-km LST from MODIS thermal infrared (TIR) bands [_{1km}_{31} and _{32} are MODIS band 31 and 32 brightness temperature (K). _{31} + _{32}) and Δ_{31} − _{32}. _{31} and _{32} are MODIS band 31 and 32 emissivity. _{1}, _{2}, _{3}, _{1}, _{2}, _{3}, and

The GSW algorithm was found to underestimate MODIS 1-km LST, especially over semi-arid and arid regions [_{1}_{km}_{1km→5km} is the daytime LST aggregated from _{1}_{km}_{5}_{km}

The Wan et al.'s approach uses the 5-km LST to correct the 1-km LST. The relatively coarse resolution of the 5-km data is probably insufficient to correct 1-km LST for the residual effects in the GSW algorithm, because there may exist large spatial variations at 1-km resolution within the 5-km grid spacing. To rectify the 5-km LST used in

The spectral radiance with surface emissivity, ^{-2}μm^{-1}sr^{-1}), ^{-2}μm^{-1}sr^{-1})(the Planck function), _{1} and _{2} are universal constants (_{1}=3.7418*10^{-16} Wm^{2}; _{2}=14388 μm K).

ASTER's TES algorithm is able to retrieve emissivity with a high accuracy of 0.015 [_{AST}_{1}_{km}_{AST}_{1}_{km}_{MOD}_{5}_{km}_{5}_{km}_{5}_{km}_{AST}_{1}_{km}

_{5km}

The Wan et al.'s approach corrects the MODIS 1-km LST with reference to the MODIS 5-km LST. The refined approach further makes use of ASTER emissivity data in order to achieve a better agreement between the ASTER and MODIS 1-km LST. Alternatively, Liu et al. proposed another approach without using MODIS 5-km data, based on the principle of the GSW algorithm [

_{s}^{2} with

If we correct the emissivities in

Assuming Δ_{c}

_{s}_{c}_{c}_{31} and _{32}, and the regression coefficients _{2}, _{3}, _{2}, and _{3}. In the case that the regression coefficients are generally unavailable to the end user, the components _{31} is available. Because the ASTER emissivity that corresponds to the MODIS emissivity _{32} is unavailable, we assume
_{c}

We used the ASTER and MODIS LST products acquired over a relief area over the Loess Plateau in China, part of the semiarid climate zone. The study area was chosen for its diverse land covers and highly variable topographical features that suffer from serious soil erosion. The dominant land covers include agricultural fields, grasslands, bare soil surfaces, forestlands, and inland water bodies. We used the same datasets as in [

ASTER surface emissivity (AST_05) and surface kinetic temperature (AST_08) products, observed on 8 June 2004, were acquired from the Earth Observing System Data Gateway (EDG). The AST_05 product has a spatial resolution of 90-m and contains band-averaged surface emissivity retrieved using the TES algorithm [

MODIS MOD11B1 and MOD11_L2 data covering the study area, on 8 June 2004, were also acquired from the EDG. The MOD11_L2 data contains LST and band-averaged emissivity in band-31 (10.780–11.280μm) and band-32 (11.770–12.270μm) generated using the GSW algorithm [_{5km}) generated using the day/night LST algorithm [_{1km→5km}) aggregated from 1-km LST retrieved from the GSW algorithm, and the band-averaged emissivity in band-31 and band-32, with a nominal resolution of 5-km [_{1}_{km}

The 90-m slope and aspect angles of the terrain area were generated from 90-m ASTER DEM data. The 1-km DEM data were generated from the 90-m DEM data by resampling with the cubic convolution algorithm [_{i}_{i}

The MODIS 1-km LST data were corrected using

The ASTER and MODIS images are referred to [

The upscaled 1-km ASTER LST from

All three correction approaches made significant effects on the original MODIS LST. The Wan et al.'s approach increased the MODIS LST from 309.8±3.57 K to 312.0±3.48 K, and reduced the ASTER-to-MODIS difference from 2.7±1.28 K to 0.6±1.84 K. The minimum MODIS LST increased from 296.2 K to 299.0 K, higher than the ASTER value. The refined approach increased the MODIS LST to 311.6±3.74 K, reducing the ASTER-to-MODIS difference to 0.9±1.50 K. The GSW algorithm based approach increased the MODIS LST to 311.7±3.61 K, reducing the difference to 0.8±1.14 K. With respect to RMSE of ASTER-to-MODIS LST difference, it decreased from 3.02 K to 1.92 K with the Wan et al.'s approach, 1.74 K with the refined approach, and 1.39 K with the GSW algorithm based approach. Overall, all the approaches reduced the discrepancy to less than 1 K on average. Among all the approaches, the Wan et al.'s approach reduced the discrepancy the most in terms of mean.

From

All three approaches corrected the original MODIS LST effectively. The Wan et al.'s approach increased the MODIS LST to 313.0±3.54 K, significantly reducing the ASTER-to-MODIS difference from 2.1±1.31 K to -0.1±1.87 K. The minimum MODIS LST increased from 296.2 K to 299.0 K, close to the ASTER value. The refined approach reduced the ASTER-to-MODIS difference to 0.2±1.57 K, and the GSW algorithm based approach reduced the ASTER-to-MODIS difference to 0.1±1.33 K. The RMSE of the ASTER-to-MODIS difference decreased from 2.44 K to 1.87 K with the Wan et al.'s approach, 1.58 K with the refined approach, and 1.34 K with the GSW algorithm based approach. Overall, all the correction approaches largely reduced the ASTER-to-MODIS discrepancy. The GSW algorithm based approach achieved the best results in terms of mean, S.D. and RMSE.

All three correction approaches reduced the ASTER-to-MODIS LST discrepancy effectively for the cases with and without terrain correction. The original ASTER-to-MODIS LST difference was 2.7±1.28 K. Without terrain correction, the corresponding reduced LST discrepancy was 0.6±1.84 K for the Wan et al.'s approach, 0.9±1.50 K for the refined approach, and 0.8±1.14 K for the GSW algorithm based approach. With terrain correction, the ASTER-to-MODIS LST discrepancy was minimized even further: -0.1±1.87 K for the Wan et al.'s approach, 0.2±1.57 K for the refined approach, and 0.1±1.33 K for the GSW algorithm based approach. The RMSE of the LST difference reduced from 3.02 K to 2.44 K for the original MODIS LST, 1.92 K to 1.87 K for the Wan et al.'s approach, 1.74 K to 1.58 K for the refined approach, and 1.39 K to 1.34 K for the GSW algorithm based approach. By comparing these values, we can deduce that the terrain effects contribution was approximately 30% of the total discrepancy for the examined case. To achieve a better agreement between ASTER and MODIS LST, it is necessary to correct terrain induced effects for the case over rough areas.

The Wan et al.'s approach was “globally” effective in reducing the ASTER-to-MODIS LST discrepancy in terms of mean. Among all three approaches, it had the lowest

All the correction approaches can be easily implemented for the end user of MODIS and ASTER LST products. The Wan et al.'s approach requires MODIS 5-km LST data to correct the MODIS 1-km LST. It achieved good agreement with the ASTER LST in the present case. Because it does not use the ASTER data, it is not absolutely necessary for the approach to be effective in all the cases in reducing the ASTER-to-MODIS LST discrepancy. Alternatively, the refined approach makes use of both ASTER emissivity and MODIS 5-km data. It achieved better agreement with the ASTER LST than the Wan et al.'s approach did. The approach appears more complex than the GSW algorithm approach for practical use. The GSW algorithm based approach does not use MODIS 5-km data but does use ASTER emissivity data, and it requires multiple regressions to obtain the regression coefficients that are unavailable to the end user.

LST is a key variable and indicator of change in numerous environmental studies. MODIS can provide 1-km LST with almost daily coverage of the Earth; invaluable to both local- and global-scale studies. ASTER provides an 90-m resolution LST product acquired at the same time, height and nadir view as for MODIS/Terra. ASTER and MODIS offer a unique opportunity for scale-related comparative studies. However, there was found the discrepancy as large as 3 K between ASTER and MODIS LST values, even when scale difference was explicitly accounted for. The discrepancy may weaken the reliability of relevant studies that are based on ASTER and MODIS LST.

This study compared three approaches available for correcting the ASTER-to-MODIS LST discrepancy. The Wan et al.'s approach corrects the MODIS 1-km LST with reference to the MODIS 5-km LST for the errors in classification-based emissivity. The refined approach modifies the Wan et al.'s approach through incorporating ASTER emissivity and MODIS 5-km data. The GSW algorithm approach does not use MODIS 5-km but only ASTER emissivity data. All the approaches effectively reduced the LST discrepancy in the examined cases with and without terrain correction. Terrain induced effects could contribute to the total LST discrepancy by approximately 30% on average, implying that terrain correction is essential in minimizing ASTER-to-MODIS differences. With terrain correction, the original ASTER-to-MODIS LST difference reduced from 2.7±1.28 K to -0.1±1.87 K for the Wan et al.'s approach, 0.2±1.57 K for the refined approach, and 0.1±1.33 K for the GSW algorithm based approach. Among all the approaches, the GSW algorithm based method performed best in terms of mean, S.D., RMSE, and correlation coefficient.

This work is sponsored by a grant from the Ministry of Education, Culture, Sports, Science and Technology, Japan, “Dynamics of the Sun-Earth-Life Interactive System, No.G-4, the 21st Century COE Program”, and a Grant-in-Aid for scientific Research (B) (No. 17360432) from the Japan Society for the Promotion of Science. ASTER and MODIS images were obtained from EOS Data Gateway at

Scattergram of MODIS LST versus scaled ASTER LST without terrain correction for the case (a) The original MODIS MOD11_L2 LST, (b) The MODIS LST corrected using the Wan et al's approach, (c) The MODIS LST corrected using the refined approach, and (d) The MODIS LST corrected using the GSW algorithm based approach.

Scattergram of MODIS LST versus scaled ASTER LST with terrain correction for the cases (a) The original MODIS MOD11_L2 LST, (b) The MODIS LST corrected using the Wan et al's approach, (c) The MODIS LST corrected using the refined approach, and (d) The MODIS LST corrected using the GSW algorithm based approach.

Comparison of the MODIS LST (original and corrected by three approaches) with reference to the ASTER LST, without correction for terrain effects.

ASTER LST (K) | MODIS LST (K) | |||||
---|---|---|---|---|---|---|

| ||||||

original | Wan et al. | refined | GSW algorithm based | |||

Min. | 296.2 | 296.2 | 299.0 | 296.8 | 295.4 | |

Max. | 320.7 | 317.3 | 321.3 | 320.5 | 318.9 | |

Mean | 312.5 | 309.8 | 312.0 | 311.6 | 311.7 | |

S.D. | 3.85 | 3.57 | 3.48 | 3.74 | 3.61 | |

| ||||||

ASTER-to-MODIS LST difference | Mean. | 2.7 | 0.6 | 0.9 | 0.8 | |

S.D. | 1.28 | 1.84 | 1.50 | 1.14 | ||

RMSE | 3.02 | 1.92 | 1.74 | 1.39 |

Comparison of the MODIS LST (original and corrected by three approaches) with reference to the ASTER LST, all corrected for terrain effects.

ASTER LST (K) | MODIS LST (K) | |||||
---|---|---|---|---|---|---|

| ||||||

original | Wan et al. | refined | GSW algorithm based | |||

Min. | 299.0 | 296.2 | 299.0 | 297.2 | 295.4 | |

Max. | 320.8 | 318.8 | 322.4 | 321.6 | 320.5 | |

Mean | 312.9 | 310.8 | 313.0 | 312.7 | 312.8 | |

S.D. | 3.74 | 3.60 | 3.54 | 3.78 | 3.67 | |

| ||||||

ASTER-to-MODIS LST difference | Mean. | 2.1 | -0.1 | 0.2 | 0.1 | |

S.D. | 1.31 | 1.87 | 1.57 | 1.33 | ||

RMSE | 2.44 | 1.87 | 1.58 | 1.34 |