Conversion of satellite passive microwave signals to land surface “skin” temperature for extremely dry deserts
Conversion of satellite passive microwave signals to land surface “skin” temperature for extremely dry deserts
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DOI：
10.1016/j.rse.2023.113857
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发表时间：
2023-12
影响因子：
13.5
通讯作者：
Peilin Song;Tianjie Zhao;Yongqiang Zhang;Qingying He
中科院分区：
文献类型：
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作者：
Peilin Song;Tianjie Zhao;Yongqiang Zhang;Qingying He
卫星被动微波（PMW）观测是获取陆面温度（LST）的重要资料来源，尤其是在多云条件下。考虑到PMW观测的空间代表性通常较差，基于PMW和基于热红外（TIR）的观测的融合被认为是生成具有适当分辨率的全天候LST的重要方法。然而，现有的基于PMW和基于TIR的观测之间的穿透差异一直是实现这一目标的障碍。沙漠地区的情况尤其如此，在沙漠地区，PMW观测的穿透深度大于其他条件下的穿透深度，因此基于TIR的信号和基于PMW的信号之间的关系更加复杂。在这方面，本研究开发了一个简单，高效的模型，适合于转换PMW为基础的地下LST的TIR类皮肤表面温度在世界各地的四个地区，其特征在于典型的干旱气候。所开发的模型采用微波派生的表层土壤水分（SSM）和植被transmittance（VT）作为主要输入，以量化的“rescent深度差”基于TIR和基于PMW的观测。结果表明，在典型的沙漠地区，该模型可以将该差异（RMSE）从>10 K显著减小到2-5 K左右。该模型的建立为今后的研究提供了一个高分辨率的全天候地表温度数据。
Satellite passive microwave (PMW) observations are important data sources for obtaining land surface temperature (LST) especially for cloudy conditions. Considering the generally poorer spatial representativeness of PMW observations, fusion of PMW-based and thermal infrared-(TIR-) based observations have been considered as an important approach to generate all-weather LST with appropriate resolution. However, the existing penetration difference between PMW-based and TIR-based observations has been an obstacle against this objective. This is especially the case for desert regions, where the penetration depth of PMW observations is larger than under other conditions and the relationship between TIR-based and PMW-based signals are thus more complicated. In this regard, this study develops an easy and high-efficiency model suitable for transforming PMW-based subsurface LST to TIR-like skin surface temperature in four regions across the world characterized by typical arid climate. The developed model employs microwave-derived surface soil moisture (SSM) and vegetation transmissivity (VT) as major inputs to quantify that “resampling depth difference” between TIR-based and PMW-based observations. Results show that the model can significantly reduce that difference (RMSE) from >10 K to around 2–5 K in the typical desert regions. The developed model is beneficial to obtaining high-resolution all-weather LST for the vast desert regions across the world in subsequent studies.