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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Spatial Downscaling of MODIS Land Surface Temperatures Using Geographically Weighted Regression: Case Study in Northern China
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Spatial Downscaling of MODIS Land Surface Temperatures Using Geographically Weighted Regression: Case Study in Northern China

机译：基于地理加权回归的MODIS地表温度空间缩减：以中国北方为例

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摘要

Land surface temperatures (LSTs) at high spatial resolution are crucial for hydrological, meteorological, and ecological studies. Downscaling LSTs from coarse resolution to finer resolution is an alternative way to obtain LSTs at high spatial resolution. In this paper, we proposed a new algorithm based on geographically weighted regression (GWR) to downscale Moderate Resolution Imaging Spectroradiometer LST data from 990 to 90 m. Unlike previous LST downscaling algorithms, this algorithm built the nonstationary relationship between LST and other environmental factors (including the normalized difference vegetation index and a digital elevation model) using geographically varying regression coefficients. The uncertainty in this algorithm was evaluated with a sensitivity analysis. The results show that the total uncertainty in this algorithm is less than 2 K. The performance of the GWR-based algorithm was assessed using concurrent ASTER LST data as a reference LST data set. Moreover, this algorithm was compared against the TsHARP algorithm, which was widely used for LST downscaling. The results indicate that the GWR-based algorithm outperforms the TsHARP algorithm in terms of statistical results. The root mean square error (mean absolute error) value decreases from 3.6 K (2.7 K) for the TsHARP algorithm to 3.1 K (2.3 K) for the GWR-based algorithm.
机译：高空间分辨率的地表温度（LST）对于水文，气象和生态学研究至关重要。将LST从粗分辨率缩减为较高分辨率是获得高空间分辨率LST的另一种方法。在本文中，我们提出了一种基于地理加权回归（GWR）的新算法，以将中分辨率成像光谱仪的LST数据从990缩小到90 m。与以前的LST缩小算法不同，该算法使用地理上变化的回归系数在LST和其他环境因素（包括归一化差异植被指数和数字高程模型）之间建立了非平稳关系。通过敏感性分析评估了该算法的不确定性。结果表明，该算法的总不确定度小于2K。使用并发ASTER LST数据作为参考LST数据集评估了基于GWR的算法的性能。此外，将该算法与被广泛用于LST缩减的TsHARP算法进行了比较。结果表明，基于GWR的算法在统计结果方面优于TsHARP算法。均方根误差（平均绝对误差）值从TsHARP算法的3.6 K（2.7 K）降低到基于GWR的算法的3.1 K（2.3 K）。

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  • 来源
    《IEEE Transactions on Geoscience and Remote Sensing》 |2016年第11期|6458-6469|共12页
  • 作者

    Si-Bo Duan; Zhao-Liang Li;

  • 作者单位

    Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China;

    Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Agricultural Sciences, Chinese Academy of Sciences, Beijing, Beijing, ChinaChina;

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  • 正文语种 eng
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  • 关键词

    Spatial resolution; Land surface temperature; MODIS; Land surface; Algorithm design and analysis; Thermal sensors;

    机译：空间分辨率;土地表面温度;MODIS;土地表面;算法设计与分析;热传感器;

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