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遥感技术与应用  2022, Vol. 37 Issue (6): 1414-1426    DOI: 10.11873/j.issn.1004-0323.2022.6.1414
土壤水分专栏     
北方典型区域遥感和模型土壤水分产品的对比及评估
黄钰玲1,2(),刘凯1,3,王树东1,王大成1,苑峰4,5,王保林4,景文4,王伟6()
1.中国科学院空天信息创新研究院,北京 100094
2.中国科学院大学,资源与环境学院,北京 100049
3.中国科学院地理科学与资源研究所,北京 100101
4.内蒙古小草数字生态产业有限公司,内蒙古 呼和浩特 010000
5.内蒙古峰茂科技创新有限公司,内蒙古 呼和浩特 010000
6.河北金融学院,河北 保定 071000
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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摘要:

多种土壤水分产品的综合评估有助于了解产品的特性与差异,对产品的算法改进及合理应用有重要意义。从空间分布,站点评估,土地覆盖类型及干湿分类等多方面对2010—2011年中国北方典型区域遥感土壤水分产品(SMOS_L3、AMSR-E_LPRM、ESACCI v04.5)和模型土壤水分产品(ECMWF_ERA5、GLDAS_Noah v2.1、GLDAS_CLSM v2.2)进行差异性及适用性分析,并从多角度讨论了影响土壤水分产品准确性的可能原因。结果表明:①在年尺度上,各产品均能有效表征西部干旱区土壤水分分布情况。在季节尺度上,ESACCI和3种模型产品夏秋季土壤水分较高且空间分布相似。②在站点评估方面,ERA5产品整体性能最优,平均相关系数R值最高为0.582,无偏均方根误差ubRMSE最低为0.045 m3/m3。模型产品在ubRMSE和R方面均优于遥感产品,能有效刻画站点观测的动态特征,但容易出现干湿偏差。ESACCI产品在遥感产品中准确性最高。AMSR-E与观测值之间的偏差最小(-0.015 m3/m3),但受天气影响其与观测值的相关性较低。SMOS产品受无线频射干扰影响,整体表现一般。③SMOS产品和AMSR-E产品分别对农田和林地最为敏感,其余产品在不同土地类型下土壤水分分布与实际情况基本一致且能较好地反映干湿分布情况。

关键词: 土壤水分评估对比SMOSAMSR-EESACCIERA5GLDAS    
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
收稿日期: 2022-03-30 出版日期: 2023-02-15
ZTFLH:  S152.7  
基金资助: 国家自然科学基金项目(42141007);内蒙古自治区科技成果转化专项资金课题(2021CG0045)
通讯作者: 王伟     E-mail: huangyuling20@mails.ucas.ac.cn;228398973@qq.com
作者简介: 黄钰玲(1998-),女,福建三明人,硕士研究生,主要从事土壤水分产品评估和干旱指数研究。E?mail: huangyuling20@mails.ucas.ac.cn
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引用本文:

黄钰玲,刘凯,王树东,王大成,苑峰,王保林,景文,王伟. 北方典型区域遥感和模型土壤水分产品的对比及评估[J]. 遥感技术与应用, 2022, 37(6): 1414-1426.

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.

链接本文:

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

图1  研究区及站点空间分布 审图号:GS(2019)1822
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
表1  遥感和模型土壤水分产品基本信息
图2  2010—2011年不同产品土壤水分的空间分布及盒须图 审图号:GS(2019)1822
图3  不同产品季节平均土壤水分的空间分布及盒须图 审图号:GS(2019)1822
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
表2  基于观测站点的遥感和模型土壤水分产品的验证结果
图4  6个站点观测值与遥感模型产品土壤水分的时间序列对比黑色柱状图为日降水数据(NST 06站点无降水数据)
图5  不同土地覆盖类型下遥感和模型产品土壤水分分布情况
图6  不同干湿等级下遥感和模型产品土壤水分分布情况
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