Radar Bistatic Configuration for Soil Moisture Estimation at L-Band Using Global Sensitivity Analysis Method

Abstract. Radar remote sensing has demonstrated its applicability to the retrieval of basin-scale soil moisture. The mechanism of radar backscattering from soils is complicated and strongly influenced by surface roughness. Additionally, retrieval of soil moisture using AIEM (advanced integrated equation model)-like models is a classic example of underdetermined problem due to a lack of credible known soil roughness distributions at a regional scale. Characterization of this roughness is therefore crucial for an accurate derivation of soil moisture based on backscattering models. This study aims to simultaneously obtain surface roughness parameters (standard deviation of surface height σ and correlation length cl) along with soil moisture from multi-angular ASAR images by using a two-step retrieval scheme based on the AIEM. The method firstly used a semi-empirical relationship that relates the roughness slope, Zs (Zs = σ2/cl) and the difference in backscattering coefficient (Δσ) from two ASAR images acquired with different incidence angles. Meanwhile, by using an experimental statistical relationship between σ and cl, both these parameters can be estimated. Then, the deduced roughness parameters were used for the retrieval of soil moisture in association with the AIEM. An evaluation of the proposed method was performed in an experimental area in the middle stream of the Heihe River Basin, where the Watershed Allied Telemetry Experimental Research (WATER) was taken place. It is demonstrated that the proposed method is feasible to achieve reliable estimation of soil water content. The key challenge is the presence of vegetation cover, which significantly impacts the estimates of surface roughness and soil moisture.

Parametric exponentially correlated surface emission model for L-band passive microwave soil moisture retrieval

To provide a reference for canopy parameters inversion, sensitivity analysis of plant canopy parameters based on remote sensing model is a prerequisite for the inversion. Because the local sensitivity analysis do not consider the coupling effect among the parameters, the EFAST (i.e., Extended Fourier Amplitude Sensitivity Test), a global sensitivity analysis, can be used not only for the analysis of each parameter, but also consider the interacted effect among each parameter. Based on PROSAIL model, the paper focused on the parameters’ sensitivity by using simulated data and EFAST method. The results showed that the EFAST considered not only the contribution of single parameter, but also the interactive effects among each parameter, and four parameters, leaf area index (LAI), leaf mesophyll structure (N), the controller factor of the average leaf slope (LIDFa) and soil moisture condition (psoil) had great effect on the canopy reflectance in the whole wavelength from 400 to 2500 nm than other canopy parameters, and the EFAST method enlarged the contribution of some parameters that had little effects.

<p>Global population is projected to keep increasing rapidly in the next 3 decades, particularly in dryland regions of the developing world, making it a global imperative to enhance crop production. However, improving current crop production in these regions is hampered by yield gaps due to poor soils, lack of irrigation and other management practices. Here we develop a crop modelling capability to help understand gaps, and apply to dryland regions where data for parametrizing and testing models is generally lacking. We present a data assimilation framework to improve simulation capability by assimilating in-situ soil moisture and vegetation data into the FAO AquaCrop model. AquaCrop is a water-driven model that simulates canopy growth, biomass and crop yield as a function of water productivity. The key strength of AquaCrop lies in the low requirement for input data thanks to its simple structure. A global sensitivity analysis is first performed using the Morris screening method and the variance-based Extended Fourier Amplitude Sensitivity Test (EFAST) method to identify the key influential parameters on the model outputs. We begin with state-only updates by assimilating different combinations of soil moisture and vegetation data (vegetation indices, biomass, etc.), and different filtering/smoothing assimilation strategies are tested. Based on the state-only assimilation results, we further evaluate the utility of joint state-parameter (augmented-states) assimilation in improving the model performance. The framework will eventually be extended to assimilate remote sensing estimates of soil moisture and vegetation data to overcome the lack of in-situ data more generally in dryland regions.</p>

Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing

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.

Spaceborne GNSS-R technology is a new remote sensing method for soil moisture monitoring. Focusing on the significant influence of water on the surface reflectivity of CYGNSS, this paper improved the removal method of water influence according to the spatial resolution of CYGNSS data. Due to the disturbance effect of the incident angle, microwave frequency and soil type on the Fresnel reflection coefficient in surface reflectivity, a normalization method of Fresnel reflection coefficient was proposed after analyzing the data characteristics of variables in the Fresnel reflection coefficient. Finally, combined with the soil moisture retrieval method of linear equation, the accuracy was compared and verified by using measured data, SMAP products and official CYGNSS products. The results indicate that the normalization method of the Fresnel reflection coefficient could effectively reduce the influence of relevant parameters on the Fresnel reflection coefficient, but the normalization effect became worse at large incident angles (greater than 65°). Compared with the official CYGNSS product, the retrieval accuracy of optimized soil moisture was improved by 10%. The method proposed in this paper will play an important reference role in the study of soil moisture retrieval using spaceborne GNSS-R data.

In this paper, we investigated the bistatic scattering of soil moisture and surface roughness of bare soil surfaces. We generated database by using an advanced integral equation model (AIEM). For better understanding the bistatic scattering characteristics of bare soil surfaces, we adopted single polarized simulations and combination of dual angular simulations. To explore the sensitivity of bistatic scattering to soil moisture, we applied a defined sensitivity index. The results shown that the scattering coefficients of VV polarization are more sensitive to soil moisture compared with the HH polarized scattering coefficients. Moreover, the forward direction is the most sensitive to soil moisture in all cases. Besides, the dual angular observations show good sensitivity to soil moisture, especially when the differences of the two incident angles are large.

This letter aimed to validate the advanced integral equation model (AIEM) through different correlation length parameterizations using radar imagery for field-scale studies in a semi-arid environment. This letter compared backscattering coefficients simulated from the AIEM and retrieved from the synthetic aperture radar imagery of a study site in Sardinia. Two treatments for the correlation length were adopted, i.e., in situ measurements and empirically based correlation length estimation. The results showed an overestimation of backscattering coefficients of 2.5 dB with a root mean square error (RMSE) of 3.1 dB for HH and VV polarizations and an underestimation of 27.7 dB and an RMSE of 31.0 dB for HV polarization from the AIEM parameterized by in situ measurements. When using the AIEM with an empirical correlation length, a bias of less than 1.0 dB was found with an RMSE of 1.7 dB for HH and VV polarizations and an overestimation of 1.1 dB and an RMSE of 5.1 dB for HV polarization. Better results were obtained with surface soil moisture (SSM) measured at 10 cm than at 5 cm. Promising soil moisture data retrieval from the SAR imagery is expected from using the empirical correlation length-parameterized AIEM for field-scale purposes in semi-arid environments.

<p>Soil moisture is very important in several disciplines such as agriculture, hydrology and meteorology. It can be mapped using active and passive microwave remote sensing techniques. From literature it is observed that quad polarized data acquired at L-band is sensitive to soil moisture and can map surface soil moisture at high spatial resolution. The main objective of this study is to analyze the potential use of L-band radar data for the retrieval of surface soil moisture over small scale agricultural areas under vegetation cover conditions. Study area selected for this study was Malavalli, village in Karnataka state India which falls in Tropical semi-arid region. Two radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the study area between 23/07/2018 and 17/09/2018 which has spatial resolution of 5m. Ground Soil moisture over 30 sample sites were collected in synchronization with satellite pass over the study area. Acquired ALOS PALSAR-2 images were processed using PolSARpro (Polarimetric SAR data Processing and Education Toolbox). ALOS PALSAR-2 has been processed and lee speckle filter is applied with window size of 3*3. Surface soil moisture distribution over small scale tomato fields are mapped by adding incidence angle using Oh Model. Incidence angle map which is not available with PolSARpro (Polarimetric SAR data Processing and Education Toolbox) software was derived using the polynomial given in the leader file which was required for oh model inversion. Study site clearly shown increasing trend of soil moisture from July to September. It is interesting to note that vegetation and urban areas are clearly discriminated in the PauliRGB images. The retrieval of soil moisture using Oh model is validated using Ground truth samples. The accuracy of Oh model over small scale tomato fields with RMSE of 1.83 m<sup>3</sup>/m<sup>-3</sup>.</p>

Soil moisture is a key component in the water cycle. Continuous observations of soil moisture over large spatial scales are important in many earth sciences applications. Current soil moisture products derived from SMOS and SMAP satellites can provide global coverage with 2-3 days cycle but have very coarse resolutions (~40 km), which limits the soil moisture products in many applications where a resolution of 1-10 km is generally needed. SAR imagery is available at high resolution and has high sensitivity to soil moisture. However the soil moisture retrieval from SAR depends on high volume of in-situ soil moisture data and is also complicated by their sensitivity to surface roughness and vegetation. This study proposes an algorithm for retrieving high resolution soil moisture by downscaling SMOS/SMAP soil moisture products using time series dual-polarized (HH and HV) Radarsat-2 data. The approach can overcome the effect of vegetation, surface roughness, and change of scales. Specifically, the effect of vegetation is removed by the water-cloud model, in which the conditions of vegetation are characterized by the backscatter coefficient of Radarsat-2 HV polarization. Time series Radarsat-2 data is used to eliminate the dependence of backscattered signal on soil surface roughness. Different mathematical models including wavelet transform are used for scale change. The algorithm is validated using in-situ soil moisture data collected in Southern Ontario in the spring and summer of 2016. The study shows promising results in soil moisture retrieval over large area.

In this paper, an empirical methodology to retrieve bare soil moisture by Synthetic Aperture Radar (SAR) is developed. The model is based on Advanced Integral Equation Model (AIEM). Since AIEM cannot express cross-polarized backscattering coefficients accurately, we propose an empirical model to retrieve soil moisture for bare farmland only with co-polarized SAR data. The soil moisture can be obtained by solving an equation of HH and VV polarized data without any field measurements. Both simulated and real SAR data are used to validate the accuracy of the model. This method is especially effective in a large area where the surface roughness is difficult to be completely measured.

ABSTRACTPROSAIL is a combination of the leaf optical properties spectra (PROSPECT) model and the scattering by arbitrarily inclined leaves (SAIL) canopy bidirectional reflectance model. When modelling forest canopy reflectance using the PROSAIL radiative transfer model, the sensitivities of parameters can affect the modelling accuracy. Traditionally, sensitivities have been assessed using local sensitivity analysis (LSA); however, drawbacks to this approach include a lack of consideration for coupled effects between different parameters. In this study, parameter sensitivities in the PROSAIL model were calculated using two global sensitivity analysis (GSA) methods (the Extended Fourier Amplitude Sensitivity Test (EFAST) method and the Morris method), field measurements, and Landsat 5 Thematic Mapper (TM) data for a Moso bamboo forest. The results of GSA were compared with those of LSA in order to identify the key parameters impacting the Moso bamboo forest canopy reflectance, and to provide a reference for model optimization and vegetation canopy inversion improvement. The results showed that: (1) the sensitivities of six major input parameters of the PROSAIL model were generally consistent with the sorting orders of the two GSA methods, but were not in accordance with those from the LSA method, especially in the mid-infrared band; (2) coupled effects among parameters acting on reflectance simulation in visible light bands were greater than those in infrared bands; (3) the simulated canopy reflectance was evaluated using Landsat 5 TM data, and the results simulated based on LSA analysis showed higher error than those based on GSA analysis, because the LSA method ignored the influence of some parameters on canopy reflectance, e.g. leaf mesophyll structure (N), average leaf angle (ALA), leaf water content (Cw), and leaf dry matter content (Cm). However, GSA was able to fully consider the coupled effects among parameters, and thus identified the sensitive parameters impacting on reflectance more accurately.

The Monitoring Underground Soil Experiment (MOUSE) 2004 was conducted at the Joint Research Centre, Ispra (Italy) in May-June 2004. During MOUSE 2004, L-band radiometric observations at vertical and horizontal polarizations of six different bare fields at five incidence angles from 25deg to 65deg were acquired. Soil moisture and temperature were registered at various depths, and surface roughness was measured with a laser profiler. The radiometric data set was processed using a laboratory-derived expression for the dielectric constant and results were compared with those obtained using dielectric constant values computed with semi-empirical models (Wang & Schmugge and Dobson et al.). Soil moisture values retrieved from the measurements using various dielectric constant models were then compared to the actual soil moisture measured at different depths (0, 5, 10, and 15 cm) to get an indication of the goodness of each model for each soil type, and the depth at which the soil moisture is being remotely sensed for each type

Considering combined or separated roughness and vegetation effects in soil moisture retrievals