Abstract
Soil moisture ( Mv ) estimation and monitoring over agricultural areas using Synthetic Aperture Radar (SAR) are often affected by vegetation cover during the growing season. Volume scattering and vegetation attenuation can complicate the received SAR backscatter signal when microwave interacts with the vegetation canopy. To address the existing problems, this article employed the model-based polarimetric decomposition method considering the two-way attenuation to remove the volume scattering and vegetation attenuation. A deorientation process of SAR data was applied to remove the influence of randomly distributed target orientation angles before the polarimetric decomposition. To parameterize the two-way attenuation, Radar Vegetation Index derived from the SAR intensity images was adopted. The Dubois model was used to describe backscattering from the underlying bare soil. Since the soil roughness parameters are difficult to measure under vegetation cover, the optimum surface roughness method was used to parameterize the Dubois model. This soil moisture retrieval algorithm was applied to the polarimetric multitemporal RADARSAT-2 SAR data over soybean fields. The validation indicates the root-mean-square error of 9.2 vol.% and 8.2 vol.% at HH and VV polarization, respectively, over the entire soybean growing period, suggesting that the proposed method is capable of reducing the effect of vegetation cover for soil moisture monitoring over the soybean field.
Highlights
S OIL moisture (Mv) is a crucial factor in many applications such as agriculture, environment, hydrology, ecology, and water management [1]–[6]
This study acknowledged the problem that soil moisture retrieval using synthetic aperture radar (SAR) in agricultural area is challenged by volume scattering and vegetation attenuation during the growing season
To compensate the effect of vegetation attenuation, a two-way attenuation parameter in water cloud model (WCM) was adopted, which is parameterized by radar vegetation index (RVI) in this study
Summary
S OIL moisture (Mv) is a crucial factor in many applications such as agriculture, environment, hydrology, ecology, and water management [1]–[6]. Primary algorithms used for soil moisture retrieval under vegetation cover are microwave radiation transfer model (RTM) and polarimetric SAR (PolSAR) decomposition. Apart from retrieving through RTMs, soil moisture can be estimated using the polarimetric model-based decomposition, which is a powerful method to interpret PolSAR signal compositions. Wang et al considered the vegetation attenuation at C-band and neglected the dihedral scattering component, which has greatly simplified the soil moisture retrieval using the polarimetric decomposition [24]. The model-based polarimetric decomposition method was used to remove the vegetation volume scattering. Since the soil surface roughness parameters greatly affect the soil moisture retrieval due to the difficulty in getting precise measurements, the optimal surface roughness method proposed by Bai et al [9] was used in this study to parameterize the Dubois model.
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