Effective Roughness Parameters Research Articles

Turbulent boundary layer profiles in airflow over young wind waves in co- and counter-wind water current

Understanding the air velocity profile over random and three-dimensional wind-waves is crucial for evaluating momentum and energy transfer between air and water. Unfortunately, determining accurate air velocity profiles in field conditions is nearly impossible. It is well accepted, though, that the airflow above water waves has a logarithmic profile usually expressed in terms of the effective roughness parameter. However, this parameter cannot be evaluated directly from the wave measurements at the water surface. For young wind waves, it was recently shown that the airflow over the waves maintains wall similarity. In this case, the airflow vertical velocity distribution can be described by the Nikuradse fully rough logarithmic profile, using the root mean square value of the water surface elevation as the equivalent sand grain roughness height. The existence of mean current in water in either co- or counter-wind direction may significantly modify the wave field, the vertical wind-velocity profile and thus the momentum and energy transfer from air to water. The effect of the water current on the spatially developing boundary layer over young wind waves and the wall similarity is examined. Combined laboratory measurements at several fetches of finely resolved mean air velocity profile above the water surface and of the characteristics of the wind-wave field are performed at multiple wind-forcing and mean water current conditions. The shear stress at the air–water interface estimated using two independent approaches is weakly dependent on current, while the resultant wave field differs significantly.

Biofilm Formation of Two Different Marine Bacteria on Modified PDMS Surfaces is Affected by Surface Roughness and Topography.

Different strategies were tested to reduce biofilm formation of the model marine bacteria Cobetia marina and Marinobacter hydrocarbonoclasticus on cross-linked polydimethylsiloxane (PDMS) coated aluminum and cellulose acetate surfaces modified by addition of multi-walled carbon nanotubes (MWCNT) or exposure of the surfaces to bromine vapors in the presence and absence of UV irradiation. The three surface modifications explored led to important reductions in biofilm formation for the two marine bacteria, up to 30% in the case of exposure to Br2(g). Biofouling reduction could be correlated to surface properties in all cases through the introduction of a quantitative theoretical model based on an effective roughness parameter, Raeff, that accounted for the different morphological changes observed. The model considers the possibility of bacterial inclusion into large surface wells, as observed by AFM in the case of Br2(g) + UV light treatment. In addition, a linear relationship was observed between biofouling reduction and the Raeff effective roughness parameter.

History effects and wall-similarity of non-equilibrium turbulent boundary layers in varying pressure gradient over rough and smooth surfaces

Experimental investigations were conducted on non-equilibrium, turbulent boundary layers over rough and smooth surfaces. A NACA 0012 airfoil was systematically rotated to generate a family of bi-directional pressure fields on the research surface. The rough surface consisted of 2mm tall, staggered, circular cylindrical elements producing flow conditions that were in the fully rough regime. The velocity fields were measured using a custom Pitot-probe boundary layer rake and time-resolved particle image velocimetry (TR-PIV). The primary effect of the roughness was to increase the magnitude of the outer layer and skin friction parameters. Flows over both wall conditions exhibited “history effects” in the integrated boundary layer parameters, which indicates characteristic dependencies on the streamwise flow evolution. Indirect skin friction methods for non-equilibrium rough wall flows were sensitive to the regression fits of the empirical data, which were used in the absence of direct measurements. The effective sandgrain roughness parameter, ks, remained independent of pressure gradient flow history, within the uncertainty of empirical methods used for skin friction determination. Varying levels of wall-similarity were observed in the smooth and rough wall Reynolds stress profiles. Although past evaluations of wall-similarity have suggested that a sufficiently large boundary layer thickness to ks is sufficient for wall-similarity, the present results indicate a need for additional multivariable considerations including matching flow histories and considering local Reynolds numbers measurement resolution.

Assessment of Effective Roughness Parameters for Simulating Sentinel-1A Observation and Retrieving Soil Moisture over Sparsely Vegetated Field

The variability of surface roughness may lead to relatively large dynamic of backscatter coefficient observed by the synthetic aperture radar (SAR), which complicates the soil moisture (SM) retrieval process based on active remote sensing. The effective roughness parameters are commonly used for parameterizing the soil scattering models, the values of which are often assumed to be constant during different study periods for the same site. This paper investigates the reasonableness of this hypothesis from the perspective of backscatter coefficient simulation and SM retrieval using high resolution SAR data. Three years of Sentinel-1A data from 2016 to 2018 were collected over a sparsely vegetated field within the REMEDHUS SM monitoring network. The advanced integral equation model (AIEM) and Dobson dielectric mixing model were combined for optimizing the effective roughness parameters, as well as simulating the backscatter coefficient and retrieving the SM. The effective roughness parameters were optimized at different temporal periods, such as 2016, 2017, 2018, 2016 + 2017, 2017 + 2018, and 2016 + 2017 + 2018, to analyze their temporal dynamics. It was found that: (1) the effective roughness parameters optimized at different temporal periods are very close to each other; (2) the simulated backscatter from AIEM is consistent with Sentinel-1A observation with root mean square errors (RMSEs) between 1.133 and 1.163 dB and correlation coefficient ® value equals to 0.616; (3) the seasonal dynamics ofin situ SM is well-captured by the retrieved SM with R values floating at 0.685 and RMSEs ranging from 0.049 to 0.052 m3/m3; and (4) inverse of the AIEM with the implementation of effective roughness parameters achieves better performance for SM retrieval than the change detection method. These findings demonstrate that the assumption on the constant effective roughness parameters during the study period of at least three years is reasonable.

Modelling and validation of surface roughness in micro-turned nickel-based alloys (Nimonic 90)

Nickel-based alloys (Nimonic 90) are one of the most used materials for aircraft parts, gas turbine components and fasteners due to their inherent properties such as high strength at elevated temperature, good corrosion resistance, high stability, high wear resistance and low thermal conductivity. Because of the above-mentioned properties, Nimonic 90 alloy is difficult to machine, and the roughness obtained by machining of nimonic alloy is comparatively rough. The existing theoretically developed mathematical equations for roughness measurement do not consist of all the machining parameters. It lacks some of the effective roughness parameters such as depth of cut, spindle speed and cutting-edge angle. This article proposes a novel mathematical/geometrical model for the prediction of surface roughness using fundamental geometrical properties of tool and workpiece. For developing the mathematical model, the nose radius of the cutting tool insert is assumed as a straight line (arc length). The principal cutting-edge angle is introduced in the geometrically developed novel model. The developed mathematical/geometrical model comprises mainly depth of cut, principal cutting-edge angle, nose radius, spindle speed and feed. In micro turning, surface roughness increases with an increase in feed and depth of cut. A rough surface, compared to conventional turning, is produced while micro turning due to edge ploughing and rubbing when the chip thickness is lesser than the edge radius. This model is validated by conducting micro-turning experiments on nickel-based superalloy (Nimonic 90) using aluminium titanium nitride physical vapour deposition coated tungsten carbide micro inserts. The surface roughness is significantly affected when the cutting-edge comes in contact with the workpiece; it is because of the imperfect geometry of the nose of the cutting tool. A slight variation of surface roughness with the depth of cut has also been observed. A good correlation is observed between the predicted and experimentally measured roughness values.

Soil Moisture Retrieval Using Multistatic L-Band SAR and Effective Roughness Modeling

The interest in bistatic SAR systems for soil moisture monitoring has grown over recent years, since theoretical studies suggest that the impact of surface roughness on the retrieval of soil moisture decreases when multistatic, i.e., simultaneous mono- and bistatic, radar measurements are used. This paper presents a semi-empirical method to retrieve soil moisture over bare agricultural fields, based on effective roughness modeling, and applies it to a series of L-band fully-polarized SAR backscatter and bistatic scattering observations. The main advantage of using effective roughness parameters is that surface roughness no longer needs to be measured in the field, what is known to be the main source of error in soil moisture retrieval applications. By means of cross-validation, it is shown that the proposed method results in accurate soil moisture retrieval with an RMSE well below 0.05 m3/m3, with the best performance observed for the cross-polarized backscatter signal. In addition, different experimental SAR monostatic and bistatic configurations are evaluated in this study using the proposed retrieval technique. Results illustrate that the soil moisture retrieval performance increases by using backscatter data in multiple polarizations simultaneously, compared to the case where backscatter observations in only one polarization mode are used. Furthermore, the retrieval performance of a multistatic system has been evaluated and compared to that of a traditional monostatic system. The recent BELSAR campaign (in 2018) provides time-series of experimental airborne SAR measurements in two bistatic geometries, i.e., the across-track (XTI) and along-track (ATI) flight configuration. For both configurations, bistatic observations are available in the backward region. The results show that the simultaneous use of backscatter and bistatic scattering data does not result in a profound increase in retrieval performance for the bistatic configuration flown during BELSAR 2018. As theoretical studies demonstrate a strong improvement in retrieval performance when using backscatter and bistatic scattering coefficients in the forward region simultaneously, the introduction of additional bistatic airborne campaigns with more promising multistatic SAR configurations is highly recommended.

Patterns of Difference between Physical and 1-D Calibrated Effective Roughness Parameters in Mountain Rivers

Due to the presence of boulders and different morphologies, mountain rivers contain various resistance sources. To correctly simulate river flow using 1-D hydrodynamic models, an accurate estimation of the flow resistance is required. In this article, a comparison between the physical roughness parameter (PRP) and effective roughness coefficient (ERC) is presented for three of the most typical morphological configurations in mountain rivers: cascade, step-pool, and plane-bed. The PRP and its variation were obtained through multiple measurements of field variables and an uncertainty analysis, while the ERC range was derived with a GLUE procedure implemented in HEC-RAS, a 1-D hydrodynamic model. In the GLUE experiments, two modes of the Representative Friction Slope Method (RFSM) between two cross-sections were tested, including the variation in the roughness parameter. The results revealed that the RFSM effect was limited to low flows in cascade and step-pool. Moreover, when HEC-RAS selected the RSFM, only acceptable results were presented for plane-bed. The difference between ERC and PRP depended on the flow magnitude and the morphology, and as shown in this study, when the flow increased, the ERC and PRP ranges approached each other and even overlapped in cascade and step-pool. This research aimed to improve the roughness value selection process in a 1-D model given the importance of this parameter in the predictability of the results. In addition, a comparison was presented between the results obtained with the numerical model and the values calculated with the field measurements

Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data

The objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was removed from the total backscatter by using the modified water cloud model (MWCM), which takes the vegetation fraction (fveg) into account. The VV/VH polarization radar backscattering coefficients database was established by a numerical simulation based on the advanced integrated equation model (AIEM) and the cross-polarized ratio of the Oh model. Then the empirical relationship between the bare soil backscattering coefficient and both the soil moisture and the surface roughness was developed by regression analysis. The surface roughness in this paper was described by using the effective roughness parameter and the combined roughness form. The experimental results revealed that using effective roughness as the model input instead of in-situ measured roughness can obtain soil moisture with high accuracy and effectively avoid the uncertainty of roughness measurement. The accuracy of soil moisture inversion could be improved by introducing vegetation fraction on the basis of the water cloud model (WCM). There was a good correlation between the estimated soil moisture and the observed values, with a root mean square error (RMSE) of about 4.14% and the coefficient of determination (R2) about 0.7390.

Retrieval of Soil Moisture by Integrating Sentinel-1A and MODIS Data over Agricultural Fields

Soil moisture is an essential variable in the land surface ecosystem, which plays an important role in agricultural drought monitoring, crop status monitoring, and crop yield prediction. High-resolution radar data can be combined with optical remote-sensing data to provide a new approach to estimate high-resolution soil moisture over vegetated areas. In this paper, the Sentinel-1A data and the Moderate Resolution Imaging Spectroradiometer (MODIS) data are combined to retrieve soil moisture over agricultural fields. The advanced integral equation model (AIEM) is utilized to calculate the scattering contribution of the bare soil surface. The water cloud model (WCM) is applied to model the backscattering coefficient of vegetated areas, which use two vegetation parameters to parameterize the scattering and attenuation properties of vegetation. Four different vegetation parameters extracted from MODIS products are combined to predict the scattering contribution of vegetation, including the leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), normalized difference vegetation index (NDVI), and the enhanced vegetation index (EVI). The effective roughness parameters are chosen to parameterize the AIEM. The Sentinel-1A and MODIS data in 2017 are used to calibrate the coupled model, and the datasets in 2018 are used for soil moisture estimation. The calibration results indicate that the Sentinel-1A backscattering coefficient can be accurately predicted by the coupled model with the Pearson correlation coefficient (R) ranging from 0.58 to 0.81 and a root mean square error (RMSE) ranging from 0.996 to 1.401 dB. The modeled results show that the retrieved soil moisture can capture the seasonal dynamics of soil moisture with R ranging from 0.74 to 0.81. With the different vegetation parameter combinations used for parameterizing the scattering contribution of the canopy, the importance of suitable vegetation parameters for describing the scattering and attenuation properties of vegetation is confirmed. The LAI is recommended to characterize the scattering properties. There is no obvious clue for selecting vegetation descriptors to characterize the attenuation properties of vegetation. These promising results confirm the feasibility and validity of the coupled model for soil moisture retrieval from the Sentinel-1A and MODIS data.

Progress in simplifying hydrologic model parameterization for broad applications to post‐wildfire flooding and debris‐flow hazards

AbstractPredicting the timing of overland flow in burned watersheds can help to estimate debris‐flow timing and the location of debris‐flow initiation. Numerical models can produce flow predictions, but they are limited by our knowledge of appropriate model parameters. Moreover, opportunities to test and calibrate model parameters in post‐wildfire settings are limited by available data (measurements of debris‐flow timing are rare). In this study, we use a unique data set of rainfall and flow‐timing data to test the extent to which model parameters can be generalized from an individual watershed to other watersheds (0.01 km 2 to >1km 2) within a burned area. Simulations suggest that a single, low, saturated hydraulic conductivity value can be used in post‐wildfire landscapes with reasonable results. By contrast, we found that watershed‐scale effective Manning roughness parameter values decrease as a power‐law function of basin drainage area. Thus a Manning roughness parameter calibrated for a single basin within a burned area may not provide adequate results in a different watershed. However, when flow velocity is modeled independently for hillslopes and channels, and different roughness parameters are used for those morphometric units, there is no drainage‐area dependence on the roughness parameters. Moreover, we found that it was possible to use field‐measured grain size data to parameterize the roughness for both hillslopes and channels. Thus our results show that, employing this generalizable approach, it is possible to use field measurements to fully parameterize a model that produces peak flow timing to within a few minutes in storms lasting several hours. Further, we demonstrate how model simulations can be leveraged to identify areas within a watershed that are most susceptible to debris flows. This modeling approach could be used for decision making in hazardous burned areas and would be especially useful in ungaged basins. © 2019 John Wiley & Sons, Ltd.

Impact of the Elevation Angle on CYGNSS GNSS-R Bistatic Reflectivity as a Function of Effective Surface Roughness over Land Surfaces

The Earth’s surface bistatic reflectivity Γ L H C P , C y G N S S is experimentally characterized using the novel Global Navigation Satellite Systems Reflectometry (GNSS-R) L-band passive multistatic radar technique from the Cyclone Global Navigation Satellite Systems (CyGNSS) eight-microsatellite constellation. The focus of this study is to evaluate the influence of the GNSS satellites’ elevation angle θ e on Γ L H C P , C y G N S S , as a function of soil moisture content (SMC) and effective surface roughness parameter h . As the average response, the change of the scattering regime at a global scale and considering also vegetated surfaces appears at θ e ≈ 55°. This empirical observation is understood as a change on the dominant scattering term, from incoherent to coherent. Then, the correlation of Γ L H C P , C y G N S S and SMC is evaluated as a function of θ e over specific sparsely vegetated target areas. The smoother the surface, the higher the angular variability of the Pearson correlation coefficients. Over croplands (e.g., Argentinian Pampas), an improved correlation coefficient is achieved over angular ranges where the coherent scattering regime becomes the dominant one. As such, this function depends on the surface roughness. The maximum correlation coefficients are found at different θ e for increasing mean roughness levels: r P a m p a s ≈ 0.78 at θ e ≈ [60,70]°, r I n d i a ≈ 0.72 at θ e ≈ [50,60]°, and r S u d a n ≈ 0.74 at θ e ≈ [30,40]°. SMC retrieval algorithms based on GNSS-R multi-angular information could benefit from these findings, so as to improve the accuracy using single-polarized signals.

Reappraisal of the roughness effect parameterization schemes for L-band radiometry over bare soil

Roughness effect parameterization is critical to accurately simulate brightness temperature (Tb) signals observed by a radiometer over bare soil surface. However, current roughness parameterization schemes usually suffer from severe error, which dominates the error budget in current Tb modeling over bare soil surface. In this study, uncertainty of soil roughness parameterization schemes is comprehensively assessed using data set collected during 2004 to 2006 at the Surface Monitoring Of the Soil Reservoir Experiment (SMOSREX) bare soil experimental site. To reduce uncertainty from sampling depth mismatch, the soil moisture profile with a 1cm thickness from a calibrated Hydus-1D (H1D) model is utilized to determine the optimal soil moisture inputs to soil emission model. Uncertainties of 15 literature-based roughness effect parameterization schemes developed for L-band Tb modeling are inter-compared. The “Q/H” model is further calibrated against multi-angle and dual-polarization Tb observations at the SMOSREX bare soil site under different roughness conditions. Our results show that: (1) soil moisture sampling depth varies with soil moisture content and roughness condition. When soil is drier and rougher, the soil moisture sampling depth gets deeper. (2) The 15 roughness schemes generally perform better at vertical polarization than at horizontal polarization and better when soil surface is relative smooth than when soil surface gets rougher. The 15 roughness correction schemes have their own advantages and disadvantages with diverse error and bias characteristics. None of them has a superior performance at all conditions in terms of roughness, polarizations and incident angles. (3) A non-zero Q configuration is preferred in parameter retrieval experiments and the observed linear relationship between ΔN and root-mean-square height (σ) or σ2/LC can only be reproduced when Q is non-zero in parameter retrieval. (4) The effective roughness parameters (Q, Np and h) generally increase when soil get rougher. The calibrated Q, Nh and Nv show exponential dependence on the effective parameter h. The calibrated h still shows dependence on surface soil moisture after accounting the impact from soil sampling depth and also shows strong power-law dependence on Tb at incident angle of 40°. The non-zero-Q fitting models have comparable performance in Tb modeling with zero-Q models but may be more physically realistic.

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

The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σ°) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson’s correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m3/m3 for the ascending data, and are 0.012, 0.026, and 0.055 m3/m3 for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites.

An Algorithm for Retrieving Soil Moisture Using L-Band H-Polarized Multiangular Brightness Temperature Data

This letter presents an algorithm for retrieving soil moisture using only H-polarized multiangular brightness temperature at L-band. We developed a parameterized surface model based on a simple-empirical model, the $H_{p}$ model, for this retrieval algorithm. By analyzing a simulated database using the advanced integral equation model (AIEM), it was found that the roughness variable $H_{h}$ can be parameterized as a function of an effective roughness parameter $Sr=(k_{L}\cdot s)^{2-N}(s/l)^{N}$ . Influences of three surface roughness parameters (e.g., rms height, correlation length, and type of autocorrelation function) required to describe a rough surface on surface reflectivity were all considered in this parameterized model. Comparison with AIEM simulations over a wide range of soil conditions indicates a good performance of this model. Then, based on the $\omega-\tau$ model, this algorithm is applied on refined SMOS H-polarized multiangular brightness temperature. Retrieved soil moisture in Africa exhibits reasonable patterns and temporal changes. Validation using in situ soil moisture from Little Washita watershed and Yanco over 2010–2011 showed fine accuracy with root-mean-square errors of 0.031 and 0.045 m3/m3 for two areas, respectively.

Estimating Effective Roughness Parameters of the L-MEB Model for Soil Moisture Retrieval Using Passive Microwave Observations From SMAPVEX12

Despite the continuing efforts to improve existing soil moisture retrieval algorithms, the ability to estimate soil moisture from passive microwave observations is still hampered by problems in accurately modeling the observed microwave signal. This paper focuses on the estimation of effective surface roughness parameters of the L-band Microwave Emission from the Biosphere (L-MEB) model in order to improve soil moisture retrievals from passive microwave observations. Data from the SMAP Validation Experiment 2012 conducted in Canada are used to develop and validate a simple model for the estimation of effective roughness parameters. Results show that the L-MEB roughness parameters can be empirically related to the observed brightness temperatures and the leaf area index of the vegetation. These results indicate that the roughness parameters are compensating for both roughness and vegetation effects. It is also shown, using a leave-one-out cross validation, that the model is able to accurately estimate the roughness parameters necessary for the inversion of the L-MEB model. In order to demonstrate the usefulness of the roughness parameterization, the performance of the model is compared to more traditional roughness formulations. Results indicate that the soil moisture retrieval error can be reduced to 0.054 m3/m3 if the roughness formulation proposed in this study is implemented in the soil moisture retrieval algorithm.

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

Surface soil moisture is an important parameter in hydrology and climate investigations. Current and future satellite missions with L-band passive microwave radiometers can provide valuable information for monitoring the global soil moisture. A factor that can play a significant role in the modeling and inversion of microwave emission from land surfaces is the surface roughness. In this study, an L-band parametric emission model for exponentially correlated surfaces was developed and implemented in a soil moisture retrieval algorithm. The approach was based on the parameterization of an effective roughness parameter of Hp in relation with the geometric roughness variables (root mean square height s and correlation length l) and incidence angle. The parameterization was developed based on a large set of simulations using an analytical approach incorporated in the advanced integral equation model (AIEM) over a wide range of geophysical properties. It was found that the effective roughness parameter decreases as surface roughness increases, but increases as incidence angle increases. In contrast to previous research, Hp was found to be expressed as a function of a defined slope parameter m=s2/l, and coefficients of the function could be well described by a quadratic equation. The parametric model was then tested with L-band satellite data in soil moisture retrieval algorithm over the Little Washita watershed, which resulted in an unbiased root mean square error of about 0.03m3/m3 and 0.04m3/m3 for ascending and descending orbits, respectively.

Spatial and temporal soil moisture estimation from RADARSAT-2 imagery over Flevoland, The Netherlands

► Assess the ability of RADARSAT-2 for soil moisture retrieval over Flevoland. ► Evaluate both spatial and temporal soil moisture retrieval techniques. ► Circumvent surface roughness field measurements. ► Spatial and temporal retrievals have accuracy around 4 vol% for medium rough soils. ► Temporal retrieval performs better for smoother fields covered by stubbles. Both spatial and temporal soil moisture dynamics have a large impact on hydrologic processes in agricultural catchments. As acquiring ground measurements of soil moisture is labor intensive, it is often limited in space (to a few locations) and time (to a small number of campaigns). Remote sensing offers a useful alternative, as it allows for observing both across time and space. This study analyses the potential to retrieve spatial and temporal soil moisture information from a series of RADARSAT-2 HH- and VV-polarized C-band (5.405 GHz) backscatter observations over a large number of (nearly) bare soil agricultural fields in Flevoland, The Netherlands, acquired in the framework of AgriSAR 2009. The RADARSAT-2 backscatter observations are found to be strongly correlated with continuous soil moisture measurements recorded at two agricultural sites during the 2009 growing season, as well as with spatial soil moisture measurements conducted during three intensive field campaigns in August and September 2009. Furthermore, two different soil moisture retrieval approaches have been evaluated: a physically-based modeling approach and change detection technique. The physically-based approach makes use of the Integral Equation Model with input of effective surface roughness parameters that are updated every acquisition for each field. The soil moisture retrieval accuracy of this technique is calculated based on leave-one-out cross-validation, yielding an accuracy (RMSE) about 4 vol% for fields with medium surface roughness at both polarization schemes. For smoother fields covered by stubbles, the retrieval accuracy slightly deteriorates. The change detection technique relies on a normalization of the backscatter between dry and wet reference soil moisture measurements, yielding accuracies about 4 vol% both for fields characterized by medium roughness and stubble conditions. This study confirms the large potential of RADARSAT-2 data for the retrieval of spatial and temporal soil moisture dynamics.

Using RADARSAT-2 polarimetric and ENVISAT-ASAR dual-polarization data for estimating soil moisture over agricultural fields

There are several challenges in estimating soil moisture from radar remote sensing over agricultural fields in eastern Canada. To begin, snow cover or frozen ground is observed from November to April. From April to May, agricultural activities (e.g., ploughing and sowing) change the surface roughness from week to week thereby limiting the applicability of multitemporal and multi-incidence angle approaches. Techniques using a priori information on surface roughness are difficult to apply since the type of crop often changes from year to year. Here, we present an approach using effective roughness parameters (i.e., effective root mean square height and effective correlation length) that are obtained using an empirical relationship (independent of the crop type) between the root mean square height and the correlation length. The effective parameters allow us to resolve the Integral Equation Model for observed incidence angle and backscattering coefficient in HH and VV polarizations (σ°HH and σ°VV) using a look-up table. An additional challenge is posed by the growth of vegetation that begins in May. Three-component decompositions and radar vegetation indices were used to characterize the vegetation in agricultural fields. Surface backscattering coefficients in HH and VV polarizations (σ°SURF_HH and σ°SURF_VV) were calculated using the decompositions. An improvement in estimates of soil moisture was observed with the use of surface backscattering coefficients for bare soil and sparsely vegetated fields instead of the total backscattering.

On the Retrieval of Soil Moisture in Wheat Fields From L-Band SAR Based on Water Cloud Modeling, the IEM, and Effective Roughness Parameters

The synthetic aperture radar (SAR)-based soil moisture retrieval of agricultural fields is often hampered by vegetation effects on the backscattered signal. The semiempirical water cloud model (WCM) allows for estimating the backscatter of a vegetated surface, accounting for both the contributions of the vegetation and the underlying soil. The latter is often described through the integral equation model (IEM). Unfortunately, the IEM requires an accurate parameterization of the surface roughness which is very difficult to achieve. Therefore, this letter extends the WCM with a bare soil contribution that is based on the IEM, which, however, relies on calibrated or effective roughness parameters. Furthermore, this letter compares a number of vegetation indicators for their use in the WCM. Based on a series of L-band SAR observations, it is shown that effective roughness parameters are a promising tool for soil moisture retrieval under a wheat canopy and that the use of a leaf area index may be recommended above other vegetation indicators, as it leads to the lowest root-mean-square errors of about 5.5 vol%. These results prove the operational potential of L-band SAR data for soil moisture inferred under a wheat canopy throughout the entire crop growth cycle.

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

Abstract. Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art back\\-scatter models is not fully operational at present, mainly due to difficulties involved in the parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of field-measured roughness. This paper analyses effective roughness parameters derived from C- and L-band SAR observations over a large number of agricultural seedbed sites in Europe. It shows that param\\-eters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows for estimating effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM) for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at C- and L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.