چكيده به لاتين
Soil moisture plays an important role in hydrological, agricultural and environmental systems. For instance, it controls the water and energy exchange in the land surface and atmosphere boundary, and governs the crop growth, productivity and food security. Synthetic Aperture Radar (SAR) is widely used to estimate and monitor the spatiotemporal distribution of soil moisture, with its measurement capabilities under day/night and cloudy/clear weather conditions. In particular, surface SM has been estimated using a series of theoretical, empirical or semi-empirical models according to microwave remote sensing technology. However, most current SM retrieval models are only applicable to bare soil fields based on synthetic aperture radar (SAR) data, such as the integration equation model (IEM), improved IEM and the Oh, and Dubois electromagnetic models. In these models, the soil dielectric constant can be regarded as one of the variables. Then, the soil moisture content can be calculated by using Dobson, etc., soil dielectric constant models.
However, over agricultural areas, which are seasonally covered by different crops, the sensitivity of SAR signal to soil moisture is impacted by surface roughness and vegetation. This leads to an underdetermined issue, as several parameters regarding the soil and vegetation characteristics need to be estimated from limited observations. In such complex scenarios, the polarimetric decomposition algorithms (Cloude and Pottier, 1996) which take advantages of the full polarimetric SAR data are often used to reduce the vegetation effect and extract the ground scattering component which is related to the soil moisture. Methods for polarization decomposition include Freeman-Durden, Yamaguchi, Zhang, An, VanZly, Arii and Wang. Depending on the scattering mechanism, the models are grouped as two-component, three-component and four-component decomposition methods. Among them, the most common decomposition method is the three-component method of Freeman-Durden. This method assumes three typical scattering contributions, namely volume scattering from vegetation, surface scattering from the ground and dihedral scattering from the interaction between the ground and the vegetation. The advantages of this method are its simplicity, ease of implementation and quantitative direct elimination of the vegetation effect to retrieve the SM data. however, most of the existing model-based decomposition studies are not focused on the retrieval of physical parameters (e.g., soil moisture or trunk dielectric constant) from the outputs of the decomposition, and they do not provide any other conclusion beyond the retrieved power of every scattering mechanism and its exploitation for target detection and land classification. Some research focused on soil moisture inversion using polarimetric decomposition, which has actually been an active research line since some years ago. Nevertheless, the current state of the start on PolSAR decomposition techniques suggests that quantitative accuracy of parameters retrieved from model-based incoherent approaches is still an open issue.
this research investigates a simplified polarimetric decomposition for soil moisture retrieval over vegetated fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It aims to retrieve the soil moisture by using only the surface scattering component, once the volume scattering contribution is removed. Evaluation of the proposed simplified algorithm is performed using extensive ground measurements of soil moisture from Tonzi soilmoisture network and the time series of Sentinel-1 data. RMSE of 2.09–2.76 m3/m3 are obtained for the soil moisture retrieval based on the simplified polarimetric decomposition. The results show that the performance of soil moisture retrieval depends on modeling of volume scattering, randomly oriented vegetation had the most best result.
