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遥感技术与应用  2020, Vol. 35 Issue (1): 48-57    DOI: 10.11873/j.issn.1004-0323.2020.1.0048
土壤水分专栏     
基于SMAP亮温数据反演青藏高原玛曲区域土壤未冻水
陈家利1,2(),郑东海2(),庞国锦3,李新2
1. 兰州大学 资源环境学院,甘肃 兰州 730000
2. 中国科学院青藏高原研究所 国家青藏高原科学数据中心,北京 100101
3. 兰州交通大学 测绘与地理信息学院,甘肃 兰州 730070
Retrieval of Soil Unfrozen Water in Maqu Region of Tibetan Plateau based on SMAP Brightness Temperature Measurement
Jiali Chen1,2(),Donghai Zheng2(),Guojin Pang3,Xin Li2
1. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
2. National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
3. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
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摘要:

未冻水和冰共同存在于冻土中,两者的相互转化即冻融变化深刻影响寒区地表水分循环和能量收支。被动微波遥感技术是土壤水分监测的主要手段,但目前大多应用于非冻结土壤的水分反演,对负温环境下冻结土壤中未冻水的反演研究较少。基于SMAP卫星升轨和降轨时刻的亮温观测数据和经改进后适用于青藏高原地区的零阶微波辐射模型,利用单通道算法(SCA)和双通道算法(DCA),对青藏高原东部黄河源区玛曲区域季节冻土中的未冻水含量进行反演。结果表明:基于SMAP不同过境时刻亮温观测及不同算法的土壤未冻水反演结果均较同步地反映了研究区实测值的动态变化特征(相关系数R均大于0.9)。其中,基于SMAP降轨时刻亮温观测的反演结果在冻融交替的过渡季节存在明显低估,而基于升轨时刻亮温观测得到的反演结果精度更高。基于垂直极化亮温观测的单通道(SCA-V)和DCA算法得到的升轨时刻的反演值与实测值的无偏均方根误差(ubRMSE)分别为0.035 m3m-3和0.039 m3m-3,均达到SMAP任务的设计要求(即ubRMSE≤0.04 m3m-3),其中SCA-V对该研究区土壤未冻水的反演精度最高。与SMAP标准产品相比,基于SCA-V算法反演得到的暖季土壤水分精度更高。此外,该算法能成功反演得到冻结期土壤未冻水的动态变化,因此更适用于青藏高原地区冻融土壤条件下的水分反演。

关键词: 土壤未冻水被动微波遥感SMAP亮温青藏高原    
Abstract:

Unfrozen water and ice co-exist in frozen soil, and their mutual transformation, namely freezing-thawing change, profoundly affects the surface water circulation and energy budget in cold regions. Passive microwave remote sensing technology is the main means of soil water monitoring, but it is mostly applied to the retrieval of water in non-frozen soil, and the retrieval of unfrozen water in frozen soil under negative temperature environment is less. Based on the brightness temperature measurement data obtained from the SMAP satellite ascending and descending overpass and the improved zero-order microwave radiation model applicable to the Tibetan Plateau, using Single-Channel Algorithm (SCA) and Dual-Channel Algorithm (DCA), The content of unfrozen water in the seasonal frozen soil in Maqu region which is the source region of the Yellow River in the east of Tibetan Plateau was inverted. The results show that the in-situ measured values dynamics are better captured by the retrieval values based on the brightness temperature measurement at the different moments of SMAP satellite overpass and different algorithms of soil unfrozen water in the study area(the correlation coefficient R is greater than 0.9). Among them, the retrieval results based on the brightness temperature measurement at the SMAP descending are significantly underestimated in the transition season of freezing-thawing cycle, while the retrieval results based on the brightness temperature measurement at the SMAP ascending are more accurate. The unbiased root-mean-square error (ubRMSE) of the retrieval values which obtained based on the V-polarization Single Channel Algorithm (SCA-V) and DCA and the in-situ values is 0.035 m3m-3 and 0.039 m3m-3, respectively, which are both meet the established requirements of SMAP mission. Compared with SMAP standard products, the soil moisture in warm season obtained by retrieval based on SCA-V algorithm is more accurate in this study. In addition, the algorithm adopted in this study can successfully retrieval the dynamic change of soil unfrozen water during freezing period, so it is more suitable for the retrieval of soil moisture under freezing and thawing conditions in Tibetan Plateau.

Key words: Soil Unfrozen Water    Passive Microwave Remote Sensing    SMAP    Brightness Temperature    Tibetan Plateau
收稿日期: 2019-02-06 出版日期: 2020-04-01
ZTFLH:  TP79  
基金资助: 国家自然科学基金面上项目(41871273)
通讯作者: 郑东海     E-mail: jlchen2019@lzu.edu.cn;zhengd@itpcas.ac.cn
作者简介: 陈家利(1997-),男,四川邻水人,硕士研究生,主要从事微波遥感的研究。E?mail:jlchen2019@lzu.edu.cn
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引用本文:

陈家利,郑东海,庞国锦,李新. 基于SMAP亮温数据反演青藏高原玛曲区域土壤未冻水[J]. 遥感技术与应用, 2020, 35(1): 48-57.

Jiali Chen,Donghai Zheng,Guojin Pang,Xin Li. Retrieval of Soil Unfrozen Water in Maqu Region of Tibetan Plateau based on SMAP Brightness Temperature Measurement. Remote Sensing Technology and Application, 2020, 35(1): 48-57.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.1.0048        http://www.rsta.ac.cn/CN/Y2020/V35/I1/48

图1  玛曲土壤温湿度观测网络以及选定的SMAP中心验证网格
图2  算法流程图
图3  2016年8月至2017年7月土壤未冻水反演值与实测值的时间序列
图4  土壤未冻水的反演值与实测值的散点图
反演算法 降轨 升轨
ubRMSE/(m3/m3) Bias/(m3/m3) RMSE/(m3/m3) R ubRMSE/(m3/m3) Bias/(m3/m3) RMSE/(m3/m3) R
SCA-V 0.052 -0.027 0.059 0.92 0.035 -0.005 0.035 0.95
SCA-H 0.054 -0.035 0.065 0.91 0.049 0.007 0.049 0.94
DCA 0.052 -0.031 0.061 0.91 0.039 -0.001 0.039 0.95
表1  土壤未冻水反演值与实测值之间的误差统计表
图5  本文反演结果(SCA-V),SMAP产品与实测值(Obs)的时间序列
反演算法

ubRMSE

/(m3/m3)

Bias/(m3/m3) RMSE/(m3/m3) R
降轨 SCA-V 0.040 -0.017 0.044 0.91
SMAP Product 0.049 -0.036 0.061 0.84
升轨 SCA-V 0.032 -0.008 0.033 0.92
SMAP Product 0.043 -0.033 0.054 0.85
表2  SCA-V及SMAP Product与实测值之间的误差统计表
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