Abstract
Soil moisture plays a significant role in surface energy balance and material exchange. Synthetic aperture radar (SAR) provides a promising data source to monitor soil moisture. However, soil surface roughness is a key difficulty in bare soil moisture retrieval. To reduce the measurement error of the correlation length and improve the inversion accuracy, we used the surface roughness (Hrms, root mean surface height) and empirical correlation length lopt as proposed by Baghdadi to introduce analytical equations of the backscattering coefficient using the calibrated integral equation model (CIEM). This empirical model was developed based on analytical equations to invert soil moisture for Hrms between 0.5 and 4 cm. Experimental results demonstrated that when the incidence angle varied from 33.5° to 26.3°, R2 of the retrieved and measured soil moisture decreased from 0.67 to 0.57, and RMSE increased from 2.53% to 5.4%. Similarly, when the incidence angle varied from 33.5° to 26.3°, R2 of the retrieved and measured Hrms decreased from 0.64 to 0.51, and RMSE increased from 0.33 to 0.4 cm. Therefore, it is feasible to use the empirical model to invert soil moisture and surface roughness for bare soils. In the inversion of the soil moisture and Hrms, using Hrms and the empirical correlation length lopt as the roughness parameters in the simulations is sufficient. The empirical model has favorable validity when the incidence angle is set to 33.5° and 26.3° at the C-band.
Highlights
Soil moisture is a crucial state variable in the fields of hydrology, climatology, ecology, and agriculture [1,2,3,4]
In the inversion of the soil moisture and Hrms, using Hrms and the empirical correlation length lopt as the roughness parameters in the simulations is sufficient
When the incidence angle varied from 33.5◦ to 26.3◦, R2 of the retrieved and measured soil moisture decreased from 0.67 to 0.57, and RMSE increased from 2.53% to 5.4%
Summary
Soil moisture is a crucial state variable in the fields of hydrology, climatology, ecology, and agriculture [1,2,3,4]. El Hajj [20] developed an inversion approach to estimate surface soil moisture using sentinel-1/2 data based on the calibration of the water-cloud model. The approach attempts to use the backscattering of VV and VH polarizations and CIEM to invert soil moisture and surface roughness in bare soil. The surface roughness parameter can often be expressed as Hrms and the correlation length l. The surface roughness defined by Hrms and the empirical correlation length lopt as proposed by Baghdadi were used to introduce analytical equations of backscattering coefficient using the CIEM. Hrms and the empirical correlation length lopt were used This Parameterization empirical model was developed based analyticalERASME, equations to invert the soil. √3s/l show that using Hrms and the empirical correlation length lopt as the roughness parameters in the. The study site; (b) distribution of sampling point (Pii represents sampling point number)
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