Estimation of the Root-Zone Soil Moisture Using Passive Microwave Remote Sensing and SMAR Model
Publication: Journal of Irrigation and Drainage Engineering
Volume 143, Issue 1
Abstract
Estimation of root-zone soil moisture (RZSM) at regional scales is a critical issue in surface hydrology that could be a great help for estimating evapotranspiration, erosion, runoff, and irrigation requirements, etc. A significant number of satellites [soil moisture and ocean salinity (SMOS), special sensor microwave imager (SSM/I), advanced microwave scanning radiometer-EOS (AMSR-E), tropical rainfall measuring mission/microwave imager (TRMM/TMI), etc.] retrieve surface soil moisture (SSM) using passive microwave remote sensing. This information can be used to derive RZSM using a new mathematical filter. In particular, the recently developed soil moisture analytical relationship (SMAR) can relate the surface soil moisture to the moisture of deeper layer using a relationship derived from a soil water balance equation where infiltration is estimated based on the relative fluctuations of soil moisture in the surface soil layer. In the present paper, the SMAR model is tested on two research databases in Africa and North America [African monsoon multidisciplinary analysis (AMMA) and soil climate analysis network (SCAN), respectively], where field measurements at different depths are available. Furthermore, the TRMM/TMI Satellite is selected to retrieve the satellite SSM data of the studied regions using the land parameter retrieval model (LPRM). Both remotely sensed SSM and field measurements are used within the SMAR model to explore their ability in reproducing the RZSM and also to explore the existing difference in model parameterization moving from one dataset to the other. The SMAR model is applied using three different schemes: (1) with parameters calibrated using surface field measurements, (2) with parameters calibrated using remotely sensed SSM as input, and finally (3) using the remotely sensed SSM with the same parameters calibrated in Scheme 1. In all cases, SMAR parameters have been calibrated using a genetic algorithm optimizing the root-mean square error (RMSE) between SMAR prediction and measured RZSM. The results show that remotely sensed data may be coupled with the SMAR model to provide a good description of RZSM dynamics, but it requires a specific parameterization respect to Scheme 1. Nevertheless, it is surprising to observe that two of the four parameters of the model related to the soil texture are relatively stable moving from remote-sensed to field data.
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Acknowledgments
The authors wish to thank Ferdowsi University of Mashhad for granting this research under No. 31317. The second author thanks Dr. Salvatore Manfreda and University of Basilicata for the opportunity for cooperation. Finally authors wish to acknowledge the valuable comments from the editor and anonymous reviewers for their comments, which improved this paper.
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Journal of Irrigation and Drainage Engineering
Volume 143 • Issue 1 • January 2017
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Mar 17, 2016
Accepted: Jul 18, 2016
Published online: Sep 12, 2016
Published in print: Jan 1, 2017
Discussion open until: Feb 12, 2017
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