Validation of Soil Moisture Retrievals from SMOS Microwave Satellite

Document Type : Research Article

Authors

• mozhdeh Jamei
• mohammad mousavi
• Amin Alizadeh
• parviz irannejad

Ferdowsi university of mashhad

https://doi.org/10.22067/jsw.v31i2.59170

Abstract

Introduction: Surface soil moisture is one of the most important variables in the hydrological cycle, and plays a key role in scientific and practical applications such as hydrological modelling, weather forecasting, climate change studies and water resources managements. Microwave radiometry at low frequencies (1.4GHz) is an established technique for estimating global surface soil moisture with a suitable accuracy. In recent years, soil moisture measurements have become increasingly available from satellite-based microwave sensors. The ESA’s Soil Moisture and Ocean Salinity (SMOS) satellite was launched in November 2009. It carries the first L-band 2-D synthetic aperture microwave radiometer to provide global estimates of soil moisture with an averaged ground resolution of 43 km over the field of view. The main objective of this research was to validateSMOS soil moisture retrievals over the west and south west of Iran.
Materials and Methods:The study area is located in the west and southwest of Iran which contains five areas belongingto the Ministry of Power. For the validation of SMOS dataover the study area, the SMOS soil moisture retrievals from MIR_SMUDP2 productswere compared with ground-based insitu measurements. The validation process was carried out using Collocation techniquefor the period 2012-2013. Collocation technique is a method used in the field of remote sensing to verify compliance measurements from two or more different instruments. In this study, the collocation codes were developed in Matlab Linux programming language. The ground-based in situ measurements included direct soil moisture measurements at the 5cm depth which were collected from five meteorological stations in the study area. We prepared a file for each station which contained daily soil moisture, date and time, geographical coordinates of metrological stations as input for validation model. The SMOS Level 2 Soil Moisture User Data Product (MIR_SMUDP2 files) version 551, which were provided through the ESA, contains the retrieved soil moisture and simulated TB, dielectric constants, etc. In this work, the ESA’s SMOS Matlab tool on RedHat Linux was used to read and derivesoil moisture data from MIR_SMUDP2 files.Four statistical metrics and Taylor diagram were used for the evaluation error of validation; the Root Mean Squared Difference (RMSD), the centered Root Mean Square Difference (cRMSD), the Mean Bias Error or bias and the correlation coefficient (R).
The Taylor diagrams wereused to represent three different statistical metrics (R, centered Root Mean Square Difference (cRMSD) and standard deviation) on two dimensional plots to graphically describe how closely SMOS dataset matched ground-based observations .
Results and Discussion: Based on the research algorithm and using MATLAB, the Validation model for SMOS soil moisture data was obtained. This model was appliedfor five metrological stations and the collocated soil moisture data from SMOS data and in situ data was saved as output of model to error evaluation. The results of validation errorshoweda good correlation between the SMOS soil moisture andin situ measurements. The highestand lowest correlation coefficientswere shown at Ahvaz (R=0.88) and Sarableh(R=0.75)stations, respectively.According to the bias values, the SMOS soil moisture retrievals had underestimation atAhvaz(MBE=0.04 m3m−3),Sararod(MBE=0.011 m3m−3), Sarableh(MBE=0.048 m3m−3) stations, whereas a slight overestimation of the SMOS product was detectedatthe Darab (MBE=-0.01 m3m−3) andEkbatan (MBE=-0.031 m3m−3) stations. In addition, the Root Mean Squared Difference (RMSD) values between the SMOS data and in situ data varied from 0.02 to 0.062 m3m−3 and at Ahvaz station withRMSD=0.048 m3m−3is close to the targeted SMOS accuracy of 0.04 m3m−3.Based on the Taylor diagrams, SMOS data had the highest correlation (R=0.88) with in situ measurements at Ahwaz stationand the lowest difference (cRMSD=0.008 m3m−3) between two data setswas found at Darab station.
Conclusions:The objective of this paper was to validateESA’s SMOS (Soil Moisture and Ocean Salinity) satellite products in the west and southwest of Iran for the period of 2012-2013. The validation of SMOS soil moisture retrievals from MIR_SMUDP2 products was done by using soil moisture measurements from five meteorological stations. The SMOS soil moisture retrievals showed underestimations at Ahvaz, Sararod andSarableh stations, whereas a slight overestimation werefound at Darab, Ekbatan stations. The validation results and Taylor diagrams showed thatthe SMOS soil moisture retrievals with R=0.88, RMSD=0.048 m3m−3, cRMSD=0.021 m3m−3at Ahvaz stationwasvery close to the targeted SMOS accuracy objectiveof 0.04 m3m−3 and then at Darab station SMOS data with R=0.82, RMSD=0.028 m3m−3,cRMSD=0.008 m3m−3indicateda good agreement with ground soil moisture measurements. Overall, the SMOS soil moisture data hadan acceptableaccuracy and agreement with in situ data at all stations. Therefore, we can use these data sets as a tool to derive soil moisture maps at study areas.

Keywords

• L- Band
• Microwave Radiometry
• MIR_SMUDP2 Product
• Remote sensing
• Soil Moisture Map

References

1- Al-Yaari A., Wigneron, J.-P., Ducharne, A., Kerr, Y., De Rosnay, P., De Jeu, R., Govind, A., Al Bitar, A., Albergel, C., and Munoz-Sabater J. 2014. Global-scale evaluation of two satellite-based passive microwave soil moisture datasets (SMOS and AMSR-E) with respect to Land Data Assimilation System estimates, Remote Sensing of Environment, 149: 181-195.

2- Al Bitar A., Leroux, D., Kerr, Y. H., Merlin, O., Richaume, P., Sahoo, A., and Wood E. F. 2012. Evaluation of SMOS soil moisture products over continental US using the SCAN/SNOTEL network, IEEE Transactions on Geoscience and Remote Sensing, 50 (5): 1572-1586.

3- Bircher S., Skou N., Kerr Y.H., and Member S. 2013.Validation of SMOS L1C and L2 Products and Important Parameters of the Retrieval Algorithm in the Skjern River Catchment , Western Denmark, IEEE Transactions on Geoscience and Remote Sensing, 51:2969-85.

4- Coopersmith E.J., Cosh M.H., Petersen W.A., Prueger J., and Niemeier J.J. 2015.Soil Moisture Model Calibration and Validation: An ARS Watershed On the South Fork of the Iowa River, Journal of Hydrometeorology, 16, 1087-1101.

5- Dall'Amico J.T.2012. Multiscale analysis of soil moisture using satellite and aircraft microwave remote sensing, in situ measurements and numerical modelling , Dissertation, University of Munich, Department of Geography, Munich, Germany.

6- Dall'Amico J.T., Schlenz F., Loew A., Mauser W. 2012. First Results of SMOS Soil Moisture Validation in the Upper Danube Catchment, IEEE Transactions on Geoscience and Remote Sensing,50:1507-16.

7- Djamai N., Magagi R., Goïta K., Hosseini M., Cosh M.H., Berg A., and Toth B. 2015. Evaluation of SMOS soil moisture products over the CanEx-SM10 area, Journal of Hydrology, 520:254-67.

8- Djamai N., Magagi R., Goita K., Merlin O., Kerr Y., and Walker A. 2015.Disaggregation of SMOS soil moisture over the Canadian Prairies, Remote Sensing of Environment,170:255-68.

9- Famiglietti J.S., Ryu D., Berg A.A., Rodell M., and Jackson T.J. 2008. Field observations of soil moisture variability across scales, Water Resources Research,44:1-16.

10- Gherboudj I., Magagi R., Goïta K., Berg A.A., Toth B., and Walker A. 2012.Validation of SMOS Data Over Agricultural and Boreal Forest Areas in Canada, IEEE Transactions on Geoscience and Remote Sensing, 50(5):1623-35.

11- Gonzalez-Zamora Á., Sanchez N., Martinez-Fernandez J., Gumuzzio Á., Piles M., Olmedo E. 2015. Long-term SMOS soil moisture products: A comprehensive evaluation across scales and methods in the Duero Basin (Spain), Physics and Chemistry of the Earth,83:123-36.

12- Holl G. 2012.Collocations Toolkit in Atmlab (version 2-1-70).

13- Jackson T.J., Bindlish R., Cosh M., and Zhao T. 2011. SMOS Soil Moisture validation with U.S. in situ networks, IEEE International Geoscience and Remote Sensing Symposium: 21-3.

14- Jackson T.J., Bindlish R., Member S., Cosh M.H., Zhao T., Member S., et al. 2012.Validation of Soil Moisture and Ocean Salinity ( SMOS ) Soil Moisture Over Watershed Networks in the U.S., IEEE Transactions on Geoscience and Remote Sensing, 50(5) :1530-43.

15- Kerr Y.H., Waldteufel P., Richaume P., Wigneron J.P., Ferrazzoli P., Mahmoodi A., et al. 2012. The SMOS soil moisture retrieval algorithm, IEEE Transactions on Geoscience and Remote Sensing, 50(5):1384-403.

16- Kerr Y.H., Waldteufel P., Wigneron J.-P., Delwart S., Cabot F.O., Boutin J., et al. 2010. The SMOS mission: New tool for monitoring key elements ofthe global water cycle, Proceedings of the IEEE,98(5):666-87.

17- Kerr Y.H., Waldteufel P., Wigneron J.-P., Martinuzzi J.-M., Font J., and Berger M. 2001. Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission, IEEE Transactions on Geoscience and Remote Sensing ,39(8):1729-35.

18- Kornelsen K.C., and Coulibaly P. 2015. Reducing multiplicative bias of satellite soil moisture retrievals, Remote Sensing of Environment, 165:109-22.

19- Leroux D.J., Kerr Y.H., Al Bitar A., Bindlish R., Jackson T.J., Berthelot B., et al. 2014.Comparison between SMOS, VUA, ASCAT, and ECMWF soil moisture products over four watersheds in U.S., IEEE Transactions on Geoscience and Remote Sensing ,52(3):1562-71.

20- Leroux D.J., Kerr Y.H., Richaume P., and Fieuzal R. 2013.Spatial distribution and possible sources of SMOS errors at the global scale, Remote Sensing of Environment,133:240-50.

21- Louvet S., Pellarin T., al Bitar A., Cappelaere B., Galle S., Grippa M., et al. 2015. SMOS soil moisture product evaluation over West-Africa from local to regional scale, Remote Sensing of Environment,156:383-94.

22- Mason P., Zillman J., Simmons A., Lindstrom E., Harrison D., Dolman H., et al. 2010. Implementation plan for the global observing system for climate in support of the UNFCCC (2010 Update), Geneva: GOOS-184, GTOS-76, WMO-TD/No. 1523, 2010.

23- McNally A., Husak G.J., Brown M., Carroll M., Funk C., Yatheendradas S., Arsenault K., Peters-Lidard C., and Verdin, J.P. 2015. Calculating Crop Water Requirement Satisfaction in the West Africa Sahel with Remotely Sensed Soil Moisture., Journal of Hydrometeorology,16:295-305.

24- Pan M., Sahoo a.K., Wood E.F., Al Bitar A., Leroux D., and Kerr Y.H. 2012. An Initial Assessment of SMOS Derived Soil Moisture over the Continental United States, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(5):1448-57.

25- Sanchez N., Martinez-fernandez J., Scaini A., and Perez-gutierrez C. 2012.Validation of the SMOS L2 Soil Moisture Data in the REMEDHUS Network ( Spain ), EEE Transactions on Geoscience and Remote Sensing, 50(5):1602-11.

26- Schalie R., Parinussa R.M., Renzullo L.J., van Dijk A.I.J.M., Su C.H., and de Jeu R.A.M. 2015. SMOS soilmoisture retrievals using the land parameter retrievalmodel: Evaluation over the Murrumbidgee Catchment, southeast Australia, Remote Sensing of Environment, 163:70-9.

27- Schalie R.v.d., Kerr Y.H., Wigneron J.P., Rodriguez-Fernandez N.J., Al-Yaari A., and Jeu R.A.M.d. 2016. Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model, International Journal of Applied Earth Observation and Geoinformation, 45:125-34.

28- Schlenz F., Dall'Amico J.T., Loew A., and Mauser W. 2012. Uncertainty Assessment of the SMOS Validation in the Upper Danube Catchment., IEEE Transactions on Geoscience and Remote Sensing, 50 (5):1517-29.

29- Su C.-H., Ryu D., Young R.I., Western A.W., and Wagner W. 2013. Inter-comparison of microwave satellite soil moisture retrievals over the Murrumbidgee Basin, southeast Australia, Remote Sensing of Environment,134:1-11.

30- Taylor K.E. 2001. Summarizing multiple aspects of model performance in a single diagram, Journal of Geophysical Research: Atmospheres, 106(D7):7183-92.

31- Wagner W., Brocca L., Naeimi V., Reichle R., Draper C., De Jeu R., et al. 2014. Clarifications on the "comparison between SMOS, VUA, ASCAT, and ECMWF Soil Moisture Products over Four Watersheds in U.S.", IEEE Transactions on Geoscience and Remote Sensing,52(3):1901-6.

32- Zeng J., Li Z., Chen Q., Bi H. 2014. Method for Soil Moisture and Surface Temperature Estimation in the Tibetan Plateau Using Spaceborne Radiometer Observations, IEEE Geoscience and Remote Sensing Letters,12(1):97-101.

33- Zeng J., Li Z., Chen Q., Bi H., Qiu J., and Zou P. 2015. Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations, Remote Sensing of Environment,163:91-110.

34- Zhao L., Yang K., Qin J., Chen Y., Tang W., Lu H., and Yang Z.L. 2014.The scale-dependence of SMOS soil moisture accuracy and its improvement through land data assimilation in the central Tibetan Plateau, Remote Sensing of Environment,152:345-55.