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Remote Sensing Technology and Application  2022, Vol. 37 Issue (6): 1414-1426    DOI: 10.11873/j.issn.1004-0323.2022.6.1414
    
Comparison and Assessment of Remote Sensing and Model-based Soil Moisture Products in Typical Regions of North China
Yuling Huang1,2(),Kai Liu1,3,Shudong Wang1,Dacheng Wang1,Feng Yuan4,5,Baolin Wang4,Wen Jing4,wei Wang6()
1.Aerospace Information Research Institute,Beijing 100094,China
2.University of Chinese Academy of Sciences,College of Resources and Environment,Beijing 100049,China
3.Institute of Geographic Sciences and Natural Resources Research,Beijing 100101,China
4.Inner Mongolia Xiaocao Digital Ecological Industry Limited Company,Hohhot 010000,China
5.Inner Mongolia Fengmao Technology Limited Company,Hohhot 010000,China
6.Hebei Finance University,Baoding 071000,China
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Abstract  

The comprehensive assessment of multiple Soil Moisture (SM) products is helpful to understand the characteristics and differences of products, and is of great significance to the algorithm improvement and rational application of products. The differences and applicability of three remote sensing SM products (SMOS_L3, AMSR-E_LPRM and ESACCI v04.5) and three model-based SM products (ECMWF_ERA5, GLDAS_Noah v2.1 and GLDAS_CLSM v2.2) in typical regions of North China from 2010 to 2011 were analyzed from the aspects of spatial distribution, in-situ evaluation, land cover type and dry and wet classification. The possible reasons affecting the accuracy of soil moisture products were discussed from multi-angle. Results show that: (1) On the annual scale, all products can effectively characterize the distribution of soil moisture in the arid region of the West. On the seasonal scale, ESACCI product and three model-based SM products had high soil moisture and similar spatial distribution in summer and autumn; (2) In terms of in-situ evaluation, ERA5 product outperformed other products with the highest average Pearson correlation coefficient (0.582) and the lowest unbiased root mean square error (0.045 m3/m3). The model-based SM products were superior to remote sensing SM products in terms of ubRMSE and R and can effectively represent the dynamic characteristics of in-situ observations. However, the time variations range of model-based SM products was low, which may lead to dry or wet bias. ESACCI product had the highest accuracy among remote sensing SM products. AMSR-E product performed well in Bias (-0.015 m3/m3), but the correlation with in-situ observations was low due to the influence of weather. SMOS product was affected by Radio-frequency Interference, and its overall performance was average; (3) SMOS and AMSR-E products were sensitive to farmland and forest respectively. The soil moisture distribution of other products under different land types was consistent with the actual situation, and can show dry and wet distribution.

Key words:  Soil moisture      Assessment      Comparison      SMOS      AMSR-E      ESACCI      ERA5      GLDAS     
Received:  30 March 2022      Published:  15 February 2023
ZTFLH:  S152.7  
Corresponding Authors:  wei Wang     E-mail:  huangyuling20@mails.ucas.ac.cn;228398973@qq.com
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Articles by authors
Yuling Huang
Kai Liu
Shudong Wang
Dacheng Wang
Feng Yuan
Baolin Wang
Wen Jing
wei Wang

Cite this article: 

Yuling Huang,Kai Liu,Shudong Wang,Dacheng Wang,Feng Yuan,Baolin Wang,Wen Jing,wei Wang. Comparison and Assessment of Remote Sensing and Model-based Soil Moisture Products in Typical Regions of North China. Remote Sensing Technology and Application, 2022, 37(6): 1414-1426.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.6.1414     OR     http://www.rsta.ac.cn/EN/Y2022/V37/I6/1414

Fig.1  Spatial distribution of study area and stations
数据时段时间分辨率空间分辨率测量深度获取链接
SMOS_L32010.01—2011.121 d25 km0—3 cmhttp:∥www.catds.fr/Products/Products-access
AMSR-E_LPRM2010.01—2011.091 d0.25°0—1 cmhttps:∥hydro1.gesdisc.eosdis.nasa.gov/data
ESACCI v04.52010.01—2011.121 d0.25°0—5 cmhttps:∥data.ceda.ac.uk/neodc/esacci
ECMWF_ERA52010.01—2011.121 h0.75°0—7 cmhttps:∥cds.climate. copernicus.eu/
GLDAS_Noah v2.12010.01—2011.123 h0.25°0—10 cmhttps:∥search.earthdata.nasa.gov/search
GLDAS_CLSM v2.22010.01—2011.121 d0.25°0—2 cmhttps:∥search.earthdata.nasa.gov/search
Table 1  Basic information of remote sensing and model-based soil moisture products
Fig.2  Spatial distribution and box plot of soil moisture of different products during 2010—2011
Fig.3  Spatial distribution and box plots of seasonal average soil moisture of different products
站点NubRMSE/(m3/m3)Bias/(m3/m3)R
SMOSAMSR-EESACCIERA5NoahCLSMSMOSAMSR- EESACCIERA5NoahCLSMSMOSAMSR-EESACCIERA5NoahCLSM
大兴320.2240.1800.0500.0430.0470.0410.242-0.0140.0170.022-0.0100.0060.137-0.051*0.408*0.460*0.463*0.408
馆陶360.2280.0910.0330.0320.0490.0400.351-0.035-0.061-0.0050.0020.003-0.0300.096*0.668*0.756*0.714*0.572
密云290.2730.2270.0650.0310.0310.0350.153-0.024-0.122-0.091-0.115-0.090-0.234-0.130-0.055*0. 700*0.717*0.621
禹城280.2260.0840.0670.0510.0600.0610.048-0.136-0.204-0.166-0.196-0.211-0.0570.344*0.508*0.769*0.633*0.705
关滩680.1530.0740.0410.0340.0390.0410.1450.1160.0600.1210.0720.0730.095*0.414*0.291*0.571*0.439*0.434
NST_01570.1070.0700.0640.0530.0620.058-0.175-0.018-0.116-0.006-0.067-0.0360.028*0.488*0.457*0.4710.235*0.298
NST_03570.1180.0890.0600.0640.0690.068-0.242-0.085-0.183-0.073-0.134-0.103-0.0930.242*0.585*0.3430.2280.166
NST_06650.1330.0950.0570.0490.0540.058-0.0240.074-0.0500.044-0.0050.0230.158*0.409*0.529*0.702*0.543*0.633
平均值0.1830.1140.0550.0450.0510.0500.062-0.015-0.082-0.019-0.056-0.0420.0000.2270.4240.5820.4970.480
Tab.2  The validation results of remote sensing and model-based soil moisture products using in-situ observations
Fig.4  Time series comparison of soil moisture between six in-situ observations and products
Fig.5  Soil moisture distribution of remote sensing and model-based products under different land cover types
Fig.6  Soil moisture distribution of remote sensing and model products under different dry and wet classification
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