An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques
An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques
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DOI：
10.3390/rs13173522
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
T. Dowling;P. Song;M. D. Jong;L. Merbold;M. Wooster;Jingfeng Huang;Yongqiang Zhang
中科院分区：
文献类型：
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作者：
T. Dowling;P. Song;M. D. Jong;L. Merbold;M. Wooster;Jingfeng Huang;Yongqiang Zhang
卫星获取的地表温度数据通常在长波红外光谱区观测到。然而，这些数据经常受到云层覆盖造成的覆盖范围的影响。填补这些“云隙”通常依赖于统计重建使用近端晴空LST像素，而这往往是一个贫穷的替代阴影LST下的云绝缘。另一种解决方案是依赖无源微波（PM）LST数据，这些数据在很大程度上不受云量影响，但其质量受到PM信号典型的非常粗糙的空间分辨率的限制。在这里，我们结合联合收割机方面，这两种方法来填补云的差距LWIR派生LST记录，肯尼亚（东非）作为我们的研究区域。建议的“云间隙填充”的方法增加了覆盖率的每日Aqua MODIS LST数据超过肯尼亚从90%。分别对原位和SEVIRI获得的LST数据进行了评估，发现该方法的均方根误差（RMSE）为2.6 K和3.6 K，相比之下，传统的基于邻近像素的统计重建方法的RMSE为4.3 K和6.7 K。我们还发现，这种精度的提高变得越来越明显时，总云量的停留时间增加，在上午到中午的时间框架。在午夜，云间隙填充性能也更好的所提出的方法，虽然RMSE的改善是远远小于（<0.3 K）在中午期间。结果表明，我们提出的两步云间隙填充方法可以提高传统云间隙填充方法的性能，并有可能扩大到提供大陆或全球范围的数据，因为它不依赖于特定于本地的知识或数据集。
Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST pixels, whilst this is often a poor surrogate for shadowed LSTs insulated under cloud. Another solution is to rely on passive microwave (PM) LST data that are largely unimpeded by cloud cover impacts, the quality of which, however, is limited by the very coarse spatial resolution typical of PM signals. Here, we combine aspects of these two approaches to fill cloud gaps in the LWIR-derived LST record, using Kenya (East Africa) as our study area. The proposed “cloud gap-filling” approach increases the coverage of daily Aqua MODIS LST data over Kenya from 90%. Evaluations were made against the in situ and SEVIRI-derived LST data respectively, revealing root mean square errors (RMSEs) of 2.6 K and 3.6 K for the proposed method by mid-day, compared with RMSEs of 4.3 K and 6.7 K for the conventional proximal-pixel-based statistical re-construction method. We also find that such accuracy improvements become increasingly apparent when the total cloud cover residence time increases in the morning-to-noon time frame. At mid-night, cloud gap-filling performance is also better for the proposed method, though the RMSE improvement is far smaller (<0.3 K) than in the mid-day period. The results indicate that our proposed two-step cloud gap-filling method can improve upon performances achieved by conventional methods for cloud gap-filling and has the potential to be scaled up to provide data at continental or global scales as it does not rely on locality-specific knowledge or datasets.