Estimation of the Root-Zone Soil Moisture Using Passive Microwave Remote Sensing and SMAR Model

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 rain- fall 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 param- eters 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

Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model

“…Following Faridani et al [32], SMAR model was applied using three different approaches. SMAR model includes various parameters, which may be estimated considering soil texture information and climate conditions in the region.…”

“…Recently, high-resolution observations of RZSM have become available through the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS), which have been compared with the microwave remote sensing observations obtained from NASA Soil Moisture Active-Passive (SMAP) mission [29,30]. A more straightforward type of RZSM modelling approach is the use of an exponential filter to predict RZSM from satellite data [31,32]. In this regard, one of the recent models is the Soil Moisture Analytical Relationship (SMAR), developed by Manfreda et al, 2014 and tested with satellite data [32][33][34][35].Despite the importance of water in arid and semi-arid regions, there are only a few studies regarding the evaluation and monitoring of environmental parameters in Iran [36][37][38][39][40].…”

“…A more straightforward type of RZSM modelling approach is the use of an exponential filter to predict RZSM from satellite data [31,32]. In this regard, one of the recent models is the Soil Moisture Analytical Relationship (SMAR), developed by Manfreda et al, 2014 and tested with satellite data [32][33][34][35].Despite the importance of water in arid and semi-arid regions, there are only a few studies regarding the evaluation and monitoring of environmental parameters in Iran [36][37][38][39][40]. In particular, a multiyear SSM dataset was extracted from SMOSL3, ECV-SM ERA-Interim and ERA-Interim/Land SSM products in 2015 over six sub regions of Iran with meteorological data characterized by different climate conditions [41].…”

“…A more straightforward type of RZSM modelling approach is the use of an exponential filter to predict RZSM from satellite data [31,32]. In this regard, one of the recent models is the Soil Moisture Analytical Relationship (SMAR), developed by Manfreda et al, 2014 and tested with satellite data [32][33][34][35].…”

A modified version of the SMAR model for estimating root-zone soil moisture from time-series of surface soil moisture

“…Results have proved the potential of this model to estimate root-zone soil moisture. Applications of the SMAR model in estimating root-zone soil moisture (RZSM) from a time series of surface soil moisture (SSM), at both local and regional scales, have proved the capability of this methodology in providing a good description of RZSM (e.g., Manfreda et al, 2014;Faridani et al, 2016;Baldwin et al, 2016). The advantage of the SMAR model over the SWI method is that there are clear physical interpretations for the SMAR parameters which can be easily determined knowing soil texture and climate of the target location.…”