Evaluation of six satellite- and model-based surface soil temperature datasets using global ground-based observations
Evaluation of six satellite- and model-based surface soil temperature datasets using global ground-based observations
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使用全球地面观测数据评估六个基于卫星和模型的表面土壤温度数据集
DOI：
10.1016/j.rse.2021.112605
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发表时间：
2021-10
影响因子：
13.5
通讯作者：
Dev Niyogi
中科院分区：
文献类型：
--
作者：
Hongliang Ma;Jiangyuan Zeng;Xiang Zhang;Peng Fu;Donghai Zheng;Jean-Pierre Wigneron;Nengcheng Chen;Dev Niyogi
基于模型和卫星的地表土壤温度（ST）产品的综合评价是将这些数据集应用于水文、生态和气候变化以及被动微波土壤湿度检索算法的先决条件。与现有的区域验证不同，本研究利用全球5个稀疏网络和15个密集网络的约800个站点的地面土壤温度观测数据，全面评估了2015年4月至2017年12月期间6个基于模型和卫星的地表温度产品。这些产品包括5个基于模式的ST，分别来自现代研究与应用回顾分析第2版（MERRA-2）、戈达德地球观测系统模式第5版正演处理（GEOS-5 FP）、全球陆地数据同化系统（GLDAS） Noah、ERA-Interim和欧洲中期天气预报中心（ECMWF）最新发布的ERA5。以及利用陆地参数检索模型（LPRM）从先进微波扫描辐射计2 （AMSR2）反演的星载ST。根据地面网络、土壤温度区间、土壤湿度区间、土地覆盖、气候带和海拔高度对这些产品的准确性进行了综合评估。结果表明，GEOS-5的平均无偏均方根差（ubRMSD）为1.84 K，是所有表面ST产品中最小的。所有基于模式的产品在捕获地面观测的时间趋势方面表现出较高的技能，平均相关系数大于0.97。与上一代ERA-Interim相比，ERA5表面ST得到了明显的改进，显示出更小的ubRMSD和绝对偏置值。所有基于模型的地表温度产品一般都低于地面温度，除了最高温度和湿度条件外，随着土壤温度和土壤湿度的增加，它们的偏差倾向于变暖。此外，大多数基于模型的产品在灌木和草地、热带、干旱和寒冷气候以及高海拔地区的性能也不稳定，ubRMSD值较大。星载LPRM ST的平均ubRMSD为3.04 K，需要进一步考虑海拔高度和下垫面对该产品的影响。这些新发现将对未来ST数据集的改进、用于从卫星数据估计土壤湿度的算法以及在各种学科中的应用具有价值。
The comprehensive evaluation of model- and satellite-based surface soil temperature (ST) products is a prerequisite for applications of these datasets in hydrology, ecology, and climate change, as well as in passive microwave soil moisture retrieval algorithms. Distinguished from existing regional validations, this study used ground soil temperature observations of approximately 800 stations from 5 sparse and 15 dense networks worldwide to fully assess six model- and satellite-based surface ST products from April 2015 to December 2017. The products consist of five model-based ST from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), the Goddard Earth Observing System Model version 5 Forward Processing (GEOS-5 FP), the Global Land Data Assimilation System (GLDAS) Noah, the ERA-Interim and the newly released ERA5 produced by the European Centre for Medium Range Weather Forecasts (ECMWF), and one satellite-based ST retrieved from the Advanced Microwave Scanning Radiometer 2 (AMSR2) by using the Land Parameter Retrieval Model (LPRM). The accuracy of these products was comprehensively assessed per availability of ground networks, soil temperature interval, soil moisture interval, land cover, climate zone, and elevation. The results show that the GEOS-5 exhibits the smallest averaged unbiased root mean square difference (ubRMSD) of 1.84 K among all the surface ST products. All model-based products show a high skill in capturing the temporal trend of ground observations with an averaged correlation coefficient larger than 0.97. The ERA5 surface ST obtains visible improvements compared to its predecessor ERA-Interim by showing smaller ubRMSD and absolute bias values. All model-based surface ST products generally show lower values than ground ST, while their bias tends to be warmer as soil temperature and soil moisture increase except for the highest temperature and moisture conditions. Moreover, unstable performance of most model-based products in shrubland and grassland, tropical, arid and cold climate, and high elevation regions is also demonstrated by larger ubRMSD values. The averaged ubRMSD of the satellite-based LPRM ST is 3.04 K, and more attentions should be paid to the impacts of elevations and underlying surfaces on improving this product. These new findings will be valuable for future refinement of ST datasets, algorithms used to estimate soil moisture from satellite data, and applications in various disciplines.
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DOI：
--
发表时间：
2014
期刊：
--
影响因子：
--
作者：
D. Entekhabi;S. Yueh;P. O’neill;K. Kellogg;A. Allen;R. Bindlish;Molly E. Brown;S. Chan;A. Colli
通讯作者：
D. Entekhabi;S. Yueh;P. O’neill;K. Kellogg;A. Allen;R. Bindlish;Molly E. Brown;S. Chan;A. Colli
影响因子：
3.8
作者：
Coopersmith, Evan J.;Cosh, Michael H.;Niemeier, James J.
通讯作者：
Niemeier, James J.
影响因子：
2.3
作者：
An-Zhi Zhang;Gen-Suo Jia;He-Song Wang;Tian-Bao Zhao
通讯作者：
An-Zhi Zhang;Gen-Suo Jia;He-Song Wang;Tian-Bao Zhao
影响因子：
4.8
作者：
J. Zeng;Zhen Li;Quan Chen;H. Bi
通讯作者：
J. Zeng;Zhen Li;Quan Chen;H. Bi
影响因子：
--
作者：
Yang Hao;He Nianpeng;Li Shenggong;Y. Guirui;Gao Yang;Wang Ruomeng
通讯作者：
Yang Hao;He Nianpeng;Li Shenggong;Y. Guirui;Gao Yang;Wang Ruomeng