Hybrid modelling of random forests and kriging with sentinel-2A multispectral imagery to determine urban brightness temperatures with high resolution
Hybrid modelling of random forests and kriging with sentinel-2A multispectral imagery to determine urban brightness temperatures with high resolution
复制标题
使用 Sentinel-2A 多光谱图像对随机森林和克里金法进行混合建模，以确定高分辨率的城市亮度温度
DOI：
10.1080/01431161.2020.1851801
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发表时间：
2020-12
影响因子：
3.4
通讯作者：
Liu Xulong
中科院分区：
文献类型：
--
作者：
Xu Jianhui;Zhang Feifei;Ruan Huihua;Hu Hongda;Liu Yan;Zhong Kaiwen;Jing Wenlong;Yang Ji;Liu Xulong
本文采用随机森林基克里格降尺度方法（RFRKriging）对广州市中心地区Landsat 8热红外遥感（TIRS）亮温（T B）进行了空间分辨率提高试验。240米Landsat 8 T B b是通过使用空间平均法从重新采样的30米Landsat 8 T B b放大的。根据T B与可见/近红外和短波红外波段光谱反射率、光谱指数（包括归一化植被指数（NDVI）、归一化建筑物指数（NDBI）和修正的归一化水指数（MNDWI））、数字高程模型（DEM）、经度、纬度、不透水面、植被和土壤组分的关系，对T B进行尺度缩小。线性回归克里格（LRKriging）和基于NDVI的热锐化算法（TsHARP）回归克里格（TsHARPKriging）也被实现。相对于Landsat 8 T B重采样的参考数据，RFRKriging在最大决定系数（R2）、最小均方根误差（RMSE）以及RMSE与相应的Landsat 8 T B标准差（RSD）之间的最小比值等方面都取得了令人满意的降尺度结果。RFRKriging捕获的道路，森林，水体和建筑物的T B差异，并增强了空间细节的缩小T B在所有城市地区。此外，结合10-m Sentinel-2A和30-m Landsat 8图像集的多类型预测变量，进一步应用RFRKriging将30-m Landsat 8 T B降尺度到10 m。然后，它被放大到30米，并与原来的30米Landsat 8 T B进行评估。RFRKriging是一种提高空间分辨率的有效而实用的方法，其RSD为0.254，RMSE为0.525K，R2为0.935。相对标准偏差小于0.5，表明RFRKriging可以达到可接受的降尺度精度。此外，RFRKriging增强了复杂城市环境中的10-m T B的空间细节，同时保持了降尺度T B的内在信息和空间模式。由于30-m和10-m T B值的真实性无法获得，本研究只能从一些理论实验中提供定量评估。此外，进行了RFRKriging降尺度T B与重新采样的Landsat 8 T B的比较。结果表明，建议的RFRKriging降尺度方法显示出更好的降尺度性能与更多的空间细节比美国国家航空航天局（NASA）中心应用的三次卷积方法。
ABSTRACT Herein, a random-forest-based Kriging downscaling method (RFRKriging) was tested to increase the spatial resolution of Landsat 8 thermal-infrared remote sensing (TIRS) brightness temperature (T b) over the central areas of Guangzhou, China. The 240-m Landsat 8 T b was upscaled from the resampled 30-m Landsat 8 T b by using the spatial averaging method. T b was downscaled based on its relationship to spectral reflectance in visible/near-infrared and shortwave infrared bands, spectral indices, including normalized difference vegetation index (NDVI), normalized difference building index (NDBI), and modified normalized difference water index (MNDWI), digital elevation model (DEM), longitude, latitude, impervious surface, vegetation, and soil fractions. The linear regression Kriging (LRKriging) and NDVI-based thermal sharpening algorithm (TsHARP) regression Kriging (TsHARPKriging) were also implemented. Relative to the reference data from the resampled Landsat 8 T b, the RFRKriging produced satisfactory downscaling results with respect to the maximum coefficient of determination (R 2), the minimum root-mean-square error (RMSE), and the minimum ratio between RMSE and the correspondent Landsat 8 T b standard deviation (RSD). RFRKriging captured T b differences over roads, forests, water bodies, and buildings and enhanced the spatial details of downscaled T b in all urban areas. Furthermore, integrating the multi-type predictor variables from 10-m Sentinel-2A and 30-m Landsat 8 image sets, RFRKriging was further applied to downscale 30-m Landsat 8 T b to 10 m. Then, it was upscaled to 30 m and assessed with the original 30-m Landsat 8 T b. RFRKriging was an effective and practical method for increasing the spatial resolution, with a low RSD of 0.254, a low RMSE of 0.525 K and a high R 2 of 0.935. The RSD of less than 0.5 indicates that RFRKriging could achieve an acceptable downscaling accuracy. Furthermore, RFRKriging enhances the spatial details of a 10-m T b in complex urban environments while maintaining the intrinsic information and spatial patterns of a downscaled T b. Due to the unavailability of the 30-m and 10-m T b truth, this study may only provide a quantitative assessment from some theoretical experiments. Also, a comparison of the RFRKriging downscaled T b with the resampled Landsat 8 T b was performed. The results suggest that the proposed RFRKriging downscaling method shows better downscaling performance with more spatial details than the cubic convolution method applied by the National Aeronautics and Space Administration (NASA) Centre.