基于局部非线性地理加权回归模型的地表温度降尺度算法研究
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国家自然科学基金(41871226);重庆市博士后特别资助(Xm2016081);重庆市气象局开放基金(KFJJ201602);中国气象局省所科技创新发展专项(SSCX201917);重庆市应用开发计划重点项目(cstc2014yykfB30003)


Research on downscaling algorithm of land surface temperature based on the non-linear geographically weighted model
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The National Natural Science Foundation of China (41871226); The Chongqing Postdoctoral Special Funding Project (Xm2016081); The Chongqing Meteorological Bureau Open Fund Project (KFJJ201602); China Meteorological Administration Provincial Science and Technology Innovation Development Project (SSCX201917); The Chongqing Municipal Application Development Program Key Project (cstc2014yykfB30003)

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    摘要:

    地表温度(land surface temperature, LST)是反映地表状况的一个重要参数,能对地表-大气相互作用过程进行描述。由于受到卫星热红外传感器成像条件的制约,获取的卫星热红外遥感图像存在时间分辨率、空间分辨率难以兼顾的问题,导致反演的LST数据难以得到深入应用。采用LST降尺度算法可以解决此矛盾,获得高时空分辨率的地表温度数据。目前LST降尺度模型逐步由全局模型转向局部模型,但局部降尺度模型忽略了非线性关系。针对此问题,提出基于局部非线性地理加权回归(non-linear geographically weighted regression, NL-GWR)的地表温度降尺度算法。选择合适的研究区域,并分别选取归一化差异植被指数(normalized difference vegetation index, NDVI)、归一化差异建筑指数(normalized difference build-up index, NDBI)以及数字高程模型(digital elevation model, DEM)作为辅助参数进行LST降尺度,将中分辨率成像光谱仪(moderate resolution imaging spectroradiometer, MODIS)地表温度空间分辨率从1 000 m提升到100 m,并将基于地理加权回归(geographically weighted regression, GWR)与NL-GWR模型的降尺度结果进行对比分析。实验结果表明,考虑非线性关系的NL-GWR模型要优于GWR线性模型,能够获得较低的均方根误差(1.96 ℃)和平均绝对误差(1.63 ℃)。

    Abstract:

    Land surface temperature (LST) is a key parameter to describe the process of surface-atmosphere interaction and reflect the surface condition. However, due to the constraints of the imaging conditions of the thermal infrared sensor, there is a contradiction in the spatial and temporal resolution of the remote sensing images. This contradiction prevents the LST data being fully utilized. The LST downscaling algorithm can effectively resolve the contradiction of spatiotemporal resolution, and obtain LST images with higher spatiotemporal resolution. The LST downscaling model is gradually studied from the global model to the local model. In the current studies, the linear relationship between the surface temperature and the auxiliary parameters is considered, however, the local nonlinear relationship is ignored. This study proposed a new algorithm based on the non-linear geographically weighted regression (NL-GWR) model to downscale the LST. This study selects the proper research area,and selects Normalized Difference Vegetation Index (NDVI), Normalized Difference Build-up Index (NDBI) and digital elevation model (DEM) respectively as an auxiliary parameters,this study proposed an LST downscaled model to downscale spatial resolution of the moderate resolution imaging spectroradiometer (MODIS) LST data from 1 000 to 100 m, and analyzed the LST downscaling results, and the results of downscaling based on geographic weighted regression (GWR) model and NL-GWR model were compared and analyzed. The experimental results show that under the same parameters, the NL-GWR model considering the nonlinear relationship is better than the GWR linear model, and the lowest root mean square error (1.96 ℃) and mean absolute error (1.63 ℃).

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罗小波,王书敏,高阳华,陈圆.基于局部非线性地理加权回归模型的地表温度降尺度算法研究[J].重庆邮电大学学报(自然科学版),2020,32(6):1003-1011.

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  • 收稿日期:2020-08-06
  • 最后修改日期:2020-11-16
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  • 在线发布日期: 2020-12-23

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