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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data
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Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS/Terra land products and MSG geostationary satellite data

机译：使用集成的MODIS / TERTA LAND产品和MSG地球静止卫星数据重建云覆盖条件下的白天陆地表面温度

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摘要

There is considerable demand for satellite observations that can support spatiotemporally continuous mapping of land surface temperature (LST) because of its strong relationships with many surface processes. However, the frequent occurrence of cloud cover induces a large blank area in current thermal infrared-based LST products. To effectively fill this blank area, a new method for reconstructing the cloud-covered LSTs of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) daytime observations is described using random forest (RF) regression approach. The high temporal resolution of the Meteosat Second Generation (MSG) LST product assisted in identifying the temporal variations in cloud cover. The cumulative downward shortwave radiation flux (DSSF) was estimated as the solar radiation factor for each MODIS pixel based on the MSG DSSF product to represent the impact from cloud cover on incident solar radiation. The RF approach was used to fit an LST linking model based on the datasets collected from clear-sky pixels that depicted the complicated relationship between LST and the predictor variables, including the surface vegetation index (the normalized difference vegetation index and the enhanced vegetation index), normalized difference water index, solar radiation factor, surface albedo, surface elevation, surface slope, and latitude. The fitted model was then used to reconstruct the LSTs of cloud-covered pixels. The proposed method was applied to the Terra/MODIS daytime LST product for four days in 2015, spanning different seasons in southwestern Europe. A visual inspection indicated that the reconstructed LSTs thoroughly captured the distribution of surface temperature associated with surface vegetation cover, solar radiation, and topography. The reconstructed LSTs showed similar spatial pattern according to the comparison with clear-sky LSTs from temporally adjacent days. In addition, evaluations against Global Land Data Assimilation System (GLDAS) NOAH 0.25 degrees 3-h LST data and reference LST data derived based on in-situ air temperature measurements showed that the reconstructed LSTs presented a stable and reliable performance. The coefficients of determination derived with the GLDAS LST data were all above 0.59 on the four examined days. These results indicate that the proposed method has a strong potential for reconstructing LSTs under cloud-covered conditions and can also accurately depict the spatial patterns of LST.
机译：由于其与许多表面过程的强大关系，可以支持卫星观测的需求，这可以支持陆地表面温度（LST）的时空连续映射。然而，频繁发生的云覆盖在当前热红外的LST产品中引起大的空白区域。为了有效地填充该空白区域，使用随机森林（RF）回归方法描述了一种重建Terra中度分辨率成像分光镜（MODIS）日间观测的云覆盖的LST的新方法。 Meteosat第二代（MSG）LST产品的高时间分辨率辅助识别云覆盖的时间变化。基于MSG DSSF产品估计累积向下的短波辐射通量（DSSF）作为每个MODIS像素的太阳辐射因子，以表示事件太阳辐射的云覆盖的影响。 RF方法用于基于从清晰天空像素收集的数据集来拟合LST连接模型，该数据集描绘了LST和预测变量之间的复杂关系，包括表面植被指数（归一化差异植被指数和增强型植被指数） ，归一化差异水指数，太阳辐射因子，表面反照，表面高度，表面坡度和纬度。然后使用拟合模型来重建云覆盖像素的LST。建议的方法应用于2015年四天的Terra / Modis Daytime LST产品，遍布欧洲西南部的不同季节。目视检查表明，重建的LST彻底捕获了与表面植被覆盖，太阳辐射和地形相关的表面温度的分布。根据与临时相邻的日子的比较，重建的LST显示了与透明天空LST的比较。此外，对全球土地数据同化系统（GLDAS）NOAH的评估为0.25度3-H LST数据和基于原位空气温度测量导出的参考LST数据显示，重建的LST呈现出稳定可靠的性能。在四个检查的日期，与GLDAS LST数据衍生的测定系数在0.59以上。这些结果表明，该方法具有在云覆盖的条件下重建LST的强大潜力，并且还可以准确地描绘LST的空间模式。

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