Optimum Surface Roughness to Parameterize Advanced Integral Equation Model for Soil Moisture Retrieval in Prairie Area Using Radarsat-2 Data

Abstract

The distribution of soil moisture is important for modeling hydrological and climatological processes to understand the Earth energy cycle and balance. The major difficulty for soil moisture retrieval in vegetated areas is how to separate the individual scattering contribution of soil moisture, vegetation, and surface roughness from the backscattered radar signal. In this paper, a semi-empirical method was proposed to retrieve soil moisture in the Ruoergai prairie using single temporal Radarsat-2 data. It was formulated by integrating the advanced integral equation model (AIEM), a semi-empirical ratio vegetation model, and optimum surface roughness parameters. The AIEM was run in the forward mode to simulate the backscattering coefficient of bare soil surface. The ratio vegetation model was applied for eliminating the vegetation effect from the observed backscattering coefficient. Meanwhile, four different vegetation parameters were used to characterize the change of vegetation: leaf area index, vegetation water content, normalized difference vegetation index, and enhanced vegetation index. A global search method was used to find out the optimum surface roughness parameters, which made the relationship between the observed and retrieved soil moisture reach up to the best. The collected in situ measurements and satellite data from the Ruoergai prairie were employed to validate the feasibility and effectiveness of the introduced method. From the analysis of experiment results, the optimum surface roughness parameters had a relative change when different vegetation parameters were used to parameterize the ratio vegetation model. In addition, the root-mean-square height had a significant impact on the accuracy of soil moisture retrieval compared with correlation length. The best retrieval result was obtained when EVI was used to remove the influence of vegetation, with a correlation coefficient of 0.84 and root-mean-square error of 4.05 vol · %. Therefore, compared with other three vegetation parameters, EVI was recommended to characterize the change of vegetation in this experiment. It was evident that optimum surface roughness parameters were validated to be a promising tool for soil moisture retrieval in prairie areas using Radarsat-2 data.Index Terms-Advanced integral equation model (AIEM), optimum surface roughness parameters, Radarsat-2, ratio vegetation model, soil moisture.

First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau

“…From the contours, it is observed that the impact of an increase in s can be balanced by an increase in l, and there may exist multiple solutions for the effective roughness parameters. These two phenomena have also been observed in the work of Bai et al [33], which were caused by an ill-posed AIEM.…”

“…The s and l are parameterized based on the effective roughness parameters, which have been used in previous studies [30] (pp. 178-180), [33] (pp. 2440-2441), and [53] (pp.…”

“…2115-2117). The validity domain of the AIEM requires ks < 3.0, where k is the wave number [12,33,[54][55][56][57]. In this paper, for C-band Sentinel-1A data, the s and l are constrained within [0.1, 2.7] cm and [5.0, 30.0] cm, respectively.…”

“…Wang et al [24] showed the feasibility of simultaneously obtaining soil moisture and roughness parameters from multi-angular ASAR imagery. Bai et al [31][32][33] combined C-band Radarsat-2 and Landsat-8 data to estimate the soil moisture in prairie areas and achieved promising results. Gherboudj et al [34] estimated the soil moisture over agricultural fields from multi-polarized and multi-angular Radarsat-2 SAR data.…”

In-Situ Measurement of Soil Permittivity at Various Depths for the Calibration and Validation of Low-Frequency SAR Soil Moisture Models by Using GPR

“…2017, 9, 580 2 of 14 the sensor type and length of the rods, the measurement depth usually varies between 5 cm and 15 cm. Most studies ignore the problems of mismatching measuring depths and utilize this kind of in situ data from low frequency L-band [1][2][3] to high frequency C- [4][5][6] and even X-band SAR data [7][8][9]. While the obtained information is generally not fully suitable for high frequency SAR data due to the limited penetration into the soil, in some cases it might also be misleading for L-band data when the soil is dry and the penetration depth exceeds the sensor depth.…”