Please wait a minute...


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
Download:  HTML  PDF (7084KB) 
Export:  BibTeX | EndNote (RIS)      

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:;
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
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.

URL:     OR

Fig.1  Spatial distribution of study area and stations
SMOS_L32010.01—2011.121 d25 km0—3 cmhttp:∥
AMSR-E_LPRM2010.01—2011.091 d0.25°0—1 cmhttps:∥
ESACCI v04.52010.01—2011.121 d0.25°0—5 cmhttps:∥
ECMWF_ERA52010.01—2011.121 h0.75°0—7 cmhttps:∥cds.climate.
GLDAS_Noah v2.12010.01—2011.123 h0.25°0—10 cmhttps:∥
GLDAS_CLSM v2.22010.01—2011.121 d0.25°0—2 cmhttps:∥
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
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
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
1 Koster R D, Dirmeyer P A, Guo Z, et al. Regions of strong coupling between soil moisture and precipitation[J]. Science, 2004,305(5687):1138-1140. DOI: .
doi: 10.1126/science.1100217
2 Tan Xiangdong, Pang Zhiguo, Jiang Wei, et al. Progress and development trend of soil moisture microwave remote sensing retrieval method[J]. Journal of Geo-information Science, 2021,23(10):1728-1742.
2 覃湘栋,庞治国,江威,等.土壤水分微波反演方法进展和发展趋势[J].地球信息科学学报,2021,23(10):1728-1742.
3 Wagner W, Hahn S, Kidd R, et al. The ASCAT soil moisture product: A review of its specifications, validation results, and emerging applications[J]. Meteorologische Zeitschrift, 2013, 22(1):5-33. DOI: .
doi: 10.1127/0941-2948/2013/0399
4 Njoku E G, Jackson T J, Lakshmi V, et al. Soil moisture retrieval from AMSR-E[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(2): 215-229. DOI: .
doi: 10.1109/TGRS.2002.808243
5 Kerr Y H, Waldteufel P, Wigneron J P, et al. The SMOS mission: New tool for monitoring key elements of the global water cycle[J]. Proceedings of the IEEE, 2010, 98(5): 666-687. DOI: .
doi: 10.1109/JPROC.2010.2043032
6 Entekhabi D, Njoku E G, O"Neill P E, et al. The Soil Moisture Active Passive (SMAP) mission[J]. Proceedings of the IEEE,2010,98(5):704-716. DOI: .
doi: 10.1109/JPROC. 2010. 2043918
7 Liu Y Y, Dorigo W A, Parinussa R M, et al. Trend-preserving blending of passive and active microwave soil moisture retrievals[J]. Remote Sensing of Environment, 2012, 123: 280-297. DOI: .
doi: 10.1016/j.rse.2012.03.014
8 Dirmeyer P A, Gao X, Zhao M, et al. GSWP-2: Multimodel analysis and implications for our perception of the land surface[J]. Bulletin of the American Meteorological Society, 2006,87(10):1381-1398.DOI: .
doi: 10.1175/BAMS-87-10-1381
9 Kim H, Parinussa R, Konings A G, et al. Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products[J]. Remote Sensing of Environment, 2018, 204: 260-275. DOI: .
doi: 10.1016/j.rse.2017.10.026
10 Zeng J, Li Z, Chen Q, et al. Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations[J]. Remote Sensing of environment, 2015, 163: 91-110. DOI: .
doi: 10.1016/j.rse.2015.03.008
11 Jiang B, Su H, Liu K, et al. Assessment of remotely sensed and modelled soil moisture data products in the US Southern Great Plains[J]. Remote Sensing, 2020, 12(12): 2030. DOI: .
doi: 10.3390/rs12122030
12 Li M, Wu P, Ma Z. A comprehensive evaluation of soil moisture and soil temperature from third‐generation atmospheric and land reanalysis data sets[J]. International Journal of Climatology,2020,40(13):5744-5766. DOI: .
doi: 10.1002/joc.6549
13 Liu Huan, Liu Ronggao, Liu Shiyang. Drought remote sensing monitoring method and its application development[J]. Journal of Geo-information Science, 2012,14(2):232-239.
13 刘欢,刘荣高,刘世阳.干旱遥感监测方法及其应用发展[J].地球信息科学学报,2012,14(2):232-239.
14 Brocca L, Melone F, Moramarco T, et al. Soil moisture temporal stability over experimental areas in Central Italy[J]. Geoderma, 2009, 148(3-4): 364-374.DOI: .
doi: 10.1016/j.geoderma.2008.11.004
15 Brocca L, Hasenauer S, Lacava T, et al. Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe[J]. Remote Sensing of Environment,2011,115(12):3390-3408. DOI: .
doi: 10. 1016/j.rse.2011.08.003
16 Draper C S, Walker J P, Steinle P J, et al. An evaluation of AMSR–E derived soil moisture over Australia[J]. Remote Sensing of Environment, 2009, 113(4): 703-710. DOI: .
doi: 10.1016/j.rse.2008.11.011
17 Lü S, Zeng Y, Wen J, et al. Estimation of penetration depth from soil effective temperature in microwave radiometry[J]. Remote Sensing,2018,10(4):519.DOI: .
doi: 10.3390/rs10040519
18 Owe M, de Jeu R, Holmes T. Multisensor historical climatology of satellite‐derived global land surface moisture[J]. Journal of Geophysical Research: Earth Surface, 2008, 113(F1). DOI: .
doi: 10.1029/2007JF000769
19 Njoku E G, Ashcroft P, Chan T K, et al. Global survey and statistics of radio-frequency interference in AMSR-E land observations[J]. IEEE Transactions on Geoscience and Remote Sensing,2005,43(5):938-947. DOI: .
doi: 10.1109/TGRS. 2004. 837507
20 Wigneron J P, Jackson T J, O'neill P, et al. Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms[J]. Remote Sensing of Environment,2017,192:238-262.DOI: .
doi: 10.1016/j.rse.2017.01.024
21 Dorigo W A, Scipal K, Parinussa R M, et al. Error characterisation of global active and passive microwave soil moisture datasets[J]. Hydrology and Earth System Sciences, 2010, 14(12): 2605-2616. DOI: .
doi: 10.5194/hessd-7-5621-2010
22 Gruber A, Scanlon T, van der Schalie R, et al. Evolution of the ESA CCI soil moisture climate data records and their underlying merging methodology[J]. Earth System Science Data, 2019, 11(2): 717-739. DOI: .
doi: 10.5194/essd-11-717-2019
23 Dorigo W A, Gruber A, De Jeu R A M, et al. Evaluation of the ESA CCI soil moisture product using ground-based observations[J]. Remote Sensing of Environment, 2015, 162: 380-395. DOI: .
doi: 10.1016/j.rse.2014.07.023
24 Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis [J]. Quarterly Journal of the Royal Meteorological Society, 2020, 146(730): 1999-2049. DOI: .
doi: 10.1002/qj.3803
25 Dee D P, Uppala S M, Simmons A J, et al. The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system[J]. Quarterly Journal of the Royal Meteorological Society,2011,137(656):553-597. DOI: .
doi: 10.1002/qj.828
26 Nijssen B, Shukla S, Lin C, et al. A prototype global drought information system based on multiple land surface models[J]. Journal of Hydrometeorology,2014,15(4):1661-1676. DOI: .
doi: 10.1175/jhm-d-13-090.1
27 Entekhabi D, Reichle R H, Koster R D, et al. Performance metrics for soil moisture retrievals and application requirements[J]. Journal of Hydrometeorology, 2010, 11(3): 832-840. DOI: .
doi: 10.1175/2010jhm1223.1
28 Ran Jinjiang, Ji Mingxia, Huang Jianping, et al. Characteristics and factors of climate change in arid and semi-arid areas over Northern China in the recent 60 years[J]. Journal of Lanzhou University (Natural Sciences Edition), 2014, 50(1): 46-53.
28 冉津江,季明霞,黄建平,等.中国北方干旱区和半干旱区近60年气候变化特征及成因分析[J]. 兰州大学学报 (自然科学版), 2014, 50(1): 46-53.
29 Ding Xu, Lai Xin, Fan Guangzhou, et al. Analysis on the applicability of reanalysis soil temperature and moisture datasets over Qinghai-Tibetan Plateau[J]. Plateau Meteorology, 2018, 37(3): 626-641.
29 丁旭,赖欣,范广洲,等.再分析土壤温湿度资料在青藏高原地区适用性的分析[J]. 高原气象, 2018, 37(3): 626-641.
30 Ma H, Zeng J, Chen N, et al. Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations[J]. Remote Sensing of Environment, 2019, 231: 111215. DOI: .
doi: 10.1016/j.rse.2019.111215
31 Al-Yaari A, Wigneron J P, Ducharne A, et al. Global-scale evaluation of two satellite-based passive microwave soil moisture datasets (SMOS and AMSR-E) with respect to Land Data Assimilation System estimates[J]. Remote Sensing of Environment,2014,149:181-195.DOI: .
doi: 10.1016/j.rse.2014.04.006
32 Wang Xiukang, Qi Xingchao, Liu Yanli,et al.Soil structure and its effect on soil water holding property under three land use patterns in piedmont plain of Mountain Tai[J]. Journal of Natural Resources,2018,33(1):63-74.
32 王修康,戚兴超,刘艳丽,等.泰山山前平原三种土地利用方式下土壤结构特征及其对土壤持水性的影响[J].自然资源学报, 2018, 33(1): 63-74.
33 Gruber A, Scanlon T, van der Schalie R, et al. Evolution of the ESA CCI soil moisture climate data records and their underlying merging methodology[J]. Earth System Science Data, 2019, 11(2): 717-739. DOI: .
doi: 10.5194/essd-11-717-2019
34 Jeu R, Wagner W, Holmes T, et al. Global soil moisture patterns observed by space borne microwave radiometers and scatterometers[J]. Surveys in Geophysics, 2008, 29(4):399-420. DOI: .
doi: 10.1007/s10712-008-9044-0
35 Wang Y W, Leng P, Peng J, et al. Global assessments of two blended microwave soil moisture products CCI and SMOPS with in-situ measurements and reanalysis data [J]. International Journal of Applied Earth Observation and Geoinformation,2021,94:102234. DOI: .
doi: 10.1016/j.jag.2020.102234
36 Lu Zheng, Chai Linna, Zhang Tao, et al. Evaluation of AMSR2 retrievals using observation of soil moisture network on the upper and middle reaches of Heihe River Basin[J]. Remote Sensing Technology and Application, 2017, 32(2): 324-337.
36 陆峥, 柴琳娜, 张涛, 等. AMSR2土壤水分产品在黑河流域中上游的验证 [J]. 遥感技术与应用, 2017, 32(2): 324-337.
37 Wang Hao, Hao Ying, Yuan Song,et al. Applicability assessment of SMAP soil moisture products in the Huaihe River Basin[J]. Remote Sensing Technology and Application, 2021, 36(5): 1009-1021.
37 王皓,郝莹,袁松,等.SMAP 土壤水分产品在淮河流域的适用性评估[J].遥感技术与应用, 2021, 36(5): 1009-1021.
[1] XU Xiao-jun,DU Hua-qiang,ZHOU Guo-mo,FAN Wen-yi. Review on Correlation Analysis of Independent Variables in Estimation Models of Vegetation Biomass Based on Remote Sensing[J]. Remote Sensing Technology and Application, 2008, 23(2): 239 -247 .
[2] Yu Wenping,Ma Mingguo. Validation of the MODIS Land Surface Temperature Products—A Case Study of the Heihe River Basin[J]. Remote Sensing Technology and Application, 2011, 26(6): 705 -712 .
[3] Li Weiwei,Husi Letu,Chen Hongbin,Shang Huazhe. Estimation of Surface Solar Radiation Using MODIS Satellite Data and RSTAR Model[J]. Remote Sensing Technology and Application, 2017, 32(4): 643 -650 .
[4] Chunliang Zhao,Wenbo Xu,Jinlong Fan. Validation of Narrow-band Surface Albedo Retrieved from FY-3C MERSI Satellite Data[J]. Remote Sensing Technology and Application, 2020, 35(1): 153 -162 .
[5] Zongsheng Zheng,Chenyu Hu,Dongmei Huang,Guoliang Zou,Zhaorong Liu,Wei Song. Research on Transfer Learning Methods for Classification of Typhoon Cloud Image[J]. Remote Sensing Technology and Application, 2020, 35(1): 202 -210 .
[6] Lingmei Jiang,Huizhen Cui,Gongxue Wang,Jianwei Yang,Jian Wang,Fangbo Pan,Xu Su,Xiyao Fang. Progress on Remote Sensing of Snow, Surface Soil Frozen/Thaw State and Soil Moisture[J]. Remote Sensing Technology and Application, 2020, 35(6): 1237 -1262 .
[7] Duo Chu,Caiwang Dunzhu,Lawang Dunzhu,Suolang Tajie,Pingcuo Sangdan,Zhaxi Duoji,Mingma Ciren,Cuo Ping. Monitoring Glacier Avalanches in Tibet Using Sentinel-2 Imagery[J]. Remote Sensing Technology and Application, 2022, 37(6): 1289 -1301 .
[8] Jianting Huang,Na Yang,Chao Ma. Study on the Difference Characteristics between SMAP L2 Multi-scale Soil Moisture Data and ISMN Filed Measurement[J]. Remote Sensing Technology and Application, 2022, 37(6): 1392 -1403 .
[9] Shaojie Du,Tianjie Zhao,Jiancheng Shi,Chunfeng Ma,Defu Zou,Zhen Wang,Panpan Yao,Zhiqing Peng,Jingyao Zheng. Sentinel-1 and Sentinel-2 Synergistic Retrieval of Surface Soil Moisture[J]. Remote Sensing Technology and Application, 2022, 37(6): 1404 -1413 .
[10] Xiyao Fang,Lingmei Jiang,Huizhen Cui. Soil Moisture Retrieval in the Tibetan Plateau based on Sentinel-1 Radar Data[J]. Remote Sensing Technology and Application, 2022, 37(6): 1447 -1459 .