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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Downscaling land surface temperatures at regional scales with random forest regression
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Downscaling land surface temperatures at regional scales with random forest regression

机译：通过随机森林回归降低区域尺度的地表温度

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Environmental monitoring with satellite data is facilitated by frequent observations at a fine spatial scale. As land surface temperature (LST) is one environmental key variable, we implemented a random forest (RF) regression approach to increase the spatial resolution of LST maps from similar to 1 km, routinely available in daily repetition from the Moderate Resolution Imaging Spectroradiometer (MODIS), to similar to 250 m. LST was downscaled based on its relationship to topographic variables derived from digital elevation data of the Shuttle Radar Topography Mission (SRTM), land cover data (MODIS product MCD12Q1), and surface reflectances in the visible red and near infrared, which both are provided with the MODIS/Terra daily product MODO9GQ at similar to 250 m resolution. The approach was tested for a complex landscape in the Eastern Mediterranean, the Jordan River Region, with LST fields from aggregated Landsat-7 ETM + and MODIS (MODIS/Terra LST product MOD11A1) data; as reference at the finer scale, we used Landsat-7 derived LST data. For the ideal-case scenario with both degraded and reference values from the same sensor (Landsat-7 ETM +), root mean square errors (RMSE) of downscaled LSTs ranged from 1.02 K to 1.43 K for six different acquisition dates. When compared to the widely-adopted and in parallel applied TsHARP sharpening method, that is based on the relationship between NDVI and LST, downscaling accuracy with RF improved up to 19%. Applying the RF approach to MODIS LST products yielded RMSEs from 1.41 K to 1.92 K, whereas the TsHARP method and also a uniform disaggregation by resampling provided only slightly worse results. For the real MODIS LST product, downscaling with RF was affected by lower thermal contrasts in the image data that hindered an adequate training to reproduce temperature variations at the finer scale of similar to 250 m. In this context, we assume the LST product of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument (as a successor of MODIS onboard the SUOMI NPP platform) to be a better candidate for downscaling, as it provides a spatial resolution of similar to 750 m. One advantage of the RF approach is that predictor datasets can easily be adapted to data availability. In an extended RF approach with all Landsat spectral bands, downscaling results for formerly aggregated Landsat data improved distinctly and now ranged from 0.98 K to 1.33 K. This approach is also promising for the downscaling of real MODIS or VIIRS LST data as it may be combined with already available reflective data fusion models that are able to blend Landsat data and sensor data with a coarse spatial resolution (given spectral bandwidths corresponding to Landsat) to generate temporally dense synthetic Landsat time series. (C) 2016 Elsevier Inc. All rights reserved.
机译：在精细的空间尺度上进行频繁的观测有助于利用卫星数据进行环境监测。由于地表温度（LST）是一个环境关键变量，因此我们实施了随机森林（RF）回归方法，以将LST图的空间分辨率从大约1 km提高到中分辨率成像光谱仪（MODIS）的日常重复中），类似于250 m。 LST已根据与航天飞机雷达地形任务（SRTM）的数字高程数据，土地覆盖数据（MODIS产品MCD12Q1）以及可见红色和近红外的表面反射率之间的关系得出的地形变量关系进行了缩减。 MODIS / Terra日用产品MODO9GQ，分辨率约为250 m。该方法已在东约旦河地区的地中海地区进行了复杂景观测试，并使用了来自Landsat-7 ETM +和MODIS（MODIS / Terra LST产品MOD11A1）数据的LST场;作为更好的参考，我们使用了Landsat-7得出的LST数据。对于具有来自同一传感器（Landsat-7 ETM +）的退化值和参考值的理想情况，对于六个不同的采集日期，按比例缩小的LST的均方根误差（RMSE）在1.02 K至1.43 K的范围内。与基于NDVI和LST之间关系的广泛采用和并行应用的TsHARP锐化方法相比，RF的降尺度精度提高了19％。将RF方法应用于MODIS LST产品可产生从1.41 K到1.92 K的RMSE，而TsHARP方法以及通过重采样进行的统一分解仅提供了稍差的结果。对于真正的MODIS LST产品，由于图像数据中较低的热对比度而影响了RF的缩减，这妨碍了进行足够的训练以再现类似于250 m的较细尺度的温度变化。在这种情况下，我们认为可见光红外成像辐射计套件（VIIRS）仪器（作为SUOMI NPP平台上MODIS的后继产品）的LST产品是缩小尺寸的更好选择，因为它提供的空间分辨率类似于750米RF方法的优点之一是预测变量数据集可以轻松地适应数据可用性。在具有所有Landsat频谱带的扩展RF方法中，以前汇总的Landsat数据的降尺度结果得到了明显改善，现在范围从0.98 K到1.33K。这种方法也有望对实际的MODIS或VIIRS LST数据进行降尺度利用已经可用的反射数据融合模型，该模型能够将Landsat数据和传感器数据与粗糙的空间分辨率（给定的Landsat对应的频谱带宽）进行混合，以生成时间密集的合成Landsat时间序列。 （C）2016 Elsevier Inc.保留所有权利。

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