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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Method for Soil Moisture and Surface Temperature Estimation in the Tibetan Plateau Using Spaceborne Radiometer Observations
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Method for Soil Moisture and Surface Temperature Estimation in the Tibetan Plateau Using Spaceborne Radiometer Observations

机译：星载辐射计观测青藏高原土壤水分和地表温度的方法

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摘要

A method for soil moisture and surface temperature estimation in the Tibetan Plateau (TP) using spaceborne radiometer observations was presented. Based on the physical basis that the 36.5-GHz (Ka-band) vertical brightness temperature is highly sensitive to the topsoil temperature, a new surface temperature model was developed using all ground measurements available from three networks named CAMP/Tibet, Maqu, and Naqu, established in the TP, which can significantly improve the accuracy of surface temperature derived from the land parameter retrieval model (LPRM). Then, the new surface temperature model, which was calibrated with in situ data, was integrated into the soil moisture retrieval algorithm proposed in this letter using Advanced Microwave Scanning Radiometer (AMSR-E) observations. The algorithm combines the vegetation optical depth and roughness into an integrated factor to avoid making unreliable assumptions and using auxiliary data to get these two parameters. Finally, the algorithm was validated by ground measurements from the dense Naqu network and was compared with NASA AMSR-E and Soil Moisture and Ocean Salinity (SMOS) official algorithms. The results show that the proposed algorithm can provide much more accurate soil moisture retrievals than the other two satellite algorithms in the Naqu network region. The algorithm can be applied to the areas with spare vegetation but may not be very suitable for densely vegetated surfaces.
机译：提出了利用星载辐射计观测青藏高原土壤水分和地表温度的方法。基于36.5 GHz（Ka波段）垂直亮度温度对表土温度高度敏感的物理基础，使用来自三个名为CAMP / Tibet，Maqu和Naqu的网络的所有地面测量数据，开发了一个新的表面温度模型。在TP中建立，可以显着提高从土地参数检索模型（LPRM）得出的地表温度的准确性。然后，使用先进的微波扫描辐射计（AMSR-）将用 数据校准的新表面温度模型集成到本文中提出的土壤水分检索算法中。 E）观察。该算法将植被的光学深度和粗糙度合并为一个综合因子，以避免做出不可靠的假设，并使用辅助数据来获得这两个参数。最后，该算法通过密集的Naqu网络的地面测量得到了验证，并与NASA AMSR-E和土壤水分和海洋盐分（SMOS）官方算法进行了比较。结果表明，与那曲网络区域的其他两种卫星算法相比，该算法可以提供更准确的土壤水分反演。该算法可以应用于植被稀疏的地区，但可能不适用于茂密的植被表面。

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