Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Abstract:The Soil Moisture Active Passive (SMAP) mission was designed to provide global mapping of soil moisture (SM) on nested 3, 9, and 36 km earth grids measured by L-band passive and active microwave sensors. The validation of SMAP SM products is crucial for the application of the products and improvement of the retrieval algorithm. Since the SMAP SM products were released, much effort has been invested in the evaluation of the SMAP radiometer SM product (SMAP_P). However, there has been little validation of SMAP radar (SMAP_A) and active/passive combined (SMAP_AP) SM products. This paper presents an evaluation of SMAP_P, SMAP_A and SMAP_AP SM products by using distributed ground observations networks in different landscapes in the Heihe River Basin of northwestern China. The standard error metrics of SMAP products and relative error are applied to measure the products' performances. The results show that the SMAP SM products exhibit consistent spatial-temporal variation with the ground measurements and typical precipitation events. Three products show various types of performance capability (e.g., active, passive and combined), surface coverage (e.g., bare, vegetated) and climatic region (e.g., cold, arid). Relatively, the SMAP_P shows the best performance, while the SMAP_A performs the worst. The best performances are observed over bare soils but with overestimation and the largest relative error, and unsatisfactory accuracies are observed over cold regions and woody vegetated surfaces with underestimation. The vegetation effect and the freezing-thawing cycle may be major factors that led to an unsatisfactory performance. Efforts on resolving the influence of these factors are expected to improve the accuracy and to promote the application of SMAP SM products over these regions. Overall, this evaluation provides an understanding of SMAP SM products over cold and arid regions, and suggestions for the further refinement of the SMAP SM retrieval algorithms.

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confidence: 99%

Multi-Scale Validation of SMAP Soil Moisture Products over Cold and Arid Regions in Northwestern China Using Distributed Ground Observation Data

Long

et al. 2017

“…Figures 8 and 9 compare the IEM model with measured radar data, at 36° incidence (HH and VV polarizations) and at 26° incidence (HH polarization). Using X-band data and various incidence angles in the range between 25° and 52°, Baghdadi et al, [30] observed that the IEM correctly simulates the values of σ 0 HH and σ 0 VV for the following two cases: Hrms < 1.5 cm with an exponential correlation function; and Hrms >1.5 cm with a Gaussian function.…”

Section: Iem Modelmentioning

confidence: 99%

“…Zribi and Dechambre [31] proposed the introduction of a new parameter, Zs, equal to Hrms 2 /l, which combines the effects of two roughness descriptions (rms height and correlation length) in one single parameter, and Zribi et al [32] proposed a global parameter combining the influence of three conventional parameters (rms height, height correlation length, and correlation function shape). Lievens et al [30] showed that roughness parameters can vary from one SAR acquisition to another, since they are related to the observed backscatter coefficients, and to variations in local incidence angle. A statistical model was thus developed to estimate effective roughness parameters from radar observations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters

Gorrab

Zribi

Baghdadi³

et al. 2015

Abstract:The aim of this paper is to analyze the potential of X-band SAR measurements (COSMO-SkyMed and TerraSAR-X) made over bare soils for the estimation of soil moisture and surface geometry parameters at a semi-arid site in Tunisia (North Africa). Radar signals acquired with different configurations (HH and VV polarizations, incidence angles of 26° and 36°) are statistically compared with ground measurements (soil moisture and roughness parameters). The radar measurements are found to be highly sensitive to the various soil parameters of interest. A linear relationship is determined for the radar signals as a function of volumetric soil moisture, and a logarithmic correlation is observed between the radar signals and three surface roughness parameters: the root mean square height (Hrms), the parameter Zs = Hrms 2 /l (where l is the correlation length) and the parameter Zg = Hrms × (Hrms/l) α (where α is the power of the surface height correlation function). The highest dynamic sensitivity is observed for Zg at high incidence angles. Finally, the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models) is compared with SAR measurements. The results provide an indication of the limits of validity of the IEM and Dubois models, for various radar configurations and roughness conditions. Considerable improvements in OPEN ACCESSRemote Sens. 2015, 7 748 the IEM model performance are observed using the Baghdadi-calibrated version of this model.

“…For example, it is common to retrieve the geophysical parameters of interest, such as soil moisture and roughness, from the scattering measurements [4][5][6][7], based on analyzing the sensitivity of the scattering behavior and mechanisms. Additionally, by knowing the scattering patterns, one could effectively avoid undesired parameters, while accordingly devising a means to retrieve the parameters of interest.…”

Section: Introductionmentioning

confidence: 99%

An Information Entropy-Based Sensitivity Analysis of Radar Sensing of Rough Surface

Liu¹,

Chen²

2018

Abstract:We apply Shannon entropy, an information content measure, in sensitivity analysis (SA), stemming from the fact that the essence of SA is to preserve the maximum information content of the parameters of interest that are inverted from the radar response. Then, the sensitivity to the observation configuration and surface parameters is subsequently analyzed. Attempts are also made to explore advantages, by maximizing the information content, of dual-polarization and multi-angle in improving the parameter retrieval from radar sensing of rough surface. Simulation results show that the entropy is a good indicator of the sensitivity of the radar response to the surface parameter, as it contains information on not only the probability distribution of the scattering coefficient but also on its deviation. By information entropy, richer details, to large extent, on the scattering behavior are offered through quantitatively predicting the scattering signal saturation, evaluating the effect of using multi-polarization and multi-angle observation configuration, and identifying non-significant variables. It is found that Shannon entropy, compared to Renyi entropy, appears to better represent the sensitivity with respect to monotonic variation and narrower parameter ranges. The proposed entropy-based SA method helps to deepen our understanding of the microwave scattering behavior in response to surface parameters.