Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products.
Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products.
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DOI：
10.3390/s7123043
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发表时间：
2007-12-04
期刊：
影响因子：
--
通讯作者：
Ke C
中科院分区：
文献类型：
--
作者：
Liu Y;Yamaguchi Y;Ke C
人为引起的全球变暖显著增加了卫星监测全球地表温度的重要性。中分辨率成像光谱仪（MODIS）提供几乎每天覆盖地球的1公里分辨率LST产品，对本地和全球变化研究都是非常宝贵的。先进星载热发射反射辐射计（ASTER）提供了与MODIS在相同高度和最低点同时获得的90米高空间分辨率和16天循环周期的LST产品。ASTER和MODIS在分辨率上是互补的，为尺度相关的研究提供了独特的机会。ASTER和MODIS的地表温度被广泛使用，但地表温度的误差大多被忽视。校正ASTER-to-MODIS LST差异对于依赖于这些传感器联合使用的研究至关重要。在本研究中，我们比较了三种校正方法：Wan等人的方法、改进的Wan等人的方法和基于广义分割窗口（GSW）算法的方法。Wan等人的方法使用MODIS 5公里地表温度校正MODIS 1公里地表温度。通过结合ASTER发射率和MODIS 5公里数据，改进了Wan等人的方法。GSW算法方法不使用MODIS 5 km数据，只使用ASTER发射率数据。我们研究了中国黄土高原部分半干旱地区的情况。所有方法都有效地减小了aster - modis的LST差异。经过地形校正后，Wan等方法的ASTER-to-MODIS LST差值从2.7±1.28 K降至-0.1±1.87 K，改进方法降至0.2±1.57 K，基于GSW算法的方法降至0.1±1.33 K。在所有方法中，基于GSW算法的方法在均值、标准差、均方根和相关系数方面表现最好。
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.
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