Effect Of Soil Roughness Research Articles

Using Generalized Regression Neural Network to Retrieve Bare Surface Soil Moisture From Radarsat-2 Backscatter Observations, Regardless of Roughness Effect

The combined influence of surface soil moisture and roughness on radar backscatters has been limiting SAR’s application in soil moisture retrieval. In the past research, multi-temporal analysis and artificial neural network (ANN) inversion of physically based forward models were regarded as promising methods to decouple that combined influence. However, the former does not consider soil roughness change over a relatively longer period and the latter makes it hard to thoroughly eliminate the effect of soil roughness. This study proposes to use generalized regression neural network (GRNN) to derive bare surface soil moisture (BSSM) from radar backscatter observations regardless of the effect of soil roughness (GRNN inversion of backscatter observations). This method not only can derive BSSM from radar backscatters, provided soil roughness is unknown in any long period, but also can train models based on small-size sample data so as to reduce the manual error of training data created by simulation of physically based models. The comparison of validations between BSSM-backscatter models and BSSM-roughness-backscatter models both analyzed by GRNN shows that the incorporation of soil roughness cannot raise the prediction accuracy of models and, instead, even reduce it, indicating that the combined influence is thoroughly decoupled when being analyzed by GRNN. Moreover, BSSM-backscatter models by GRNN are recommended due to their good prediction, even compared to those related models in past publications.

Analysis of the Effect of Soil Roughness in the Forward-Scattering Interference Pattern Using Second-Order Small Perturbation Method Simulations

Soil moisture (SM) is a key geophysical variable that can be estimated at regional scales using remote sensing techniques, by making use of the known relationship between soil reflectivity and the dielectric constant in the microwave regime. In this context, the exploitation of available illuminators of opportunity that currently emit large amounts of power at microwave frequencies (compared to typical synthetic aperture radar systems) is promising. Some published techniques estimate SM by analyzing the interference pattern (IP) between direct and reflected signal as measured by a single antenna (i.e., IP technique). In this letter, a new approach to simulate the IP is proposed, in which the soil roughness is modeled straightforwardly using the second-order small perturbation model. Results illustrate that the “notch” in the VV-polarization IP (related to the Brewster angle) can only be directly observed for very low values of soil rms roughness ( $s cm). For typical values of soil roughness ( $s\sim 1.2$ cm), the notch disappears and only a minimum in the IP is observed near the Brewster angle.

Imaging Spectroscopy for Soil Mapping and Monitoring

There is a renewed awareness of the finite nature of the world’s soil resources, growing concern about soil security and significant uncertainties about the carrying capacity of the planet. Regular assessments of soil conditions from local through to global scales are requested, and there is a clear demand for accurate, up-to-date and spatially referenced soil information by the modelling scientific community, farmers and land users, and policy- and decision-makers. Soil and imaging spectroscopy, based on visible–near-infrared and shortwave infrared (400–2500 nm) spectral reflectance, has been shown to be a proven method for the quantitative prediction of key soil surface properties. With the upcoming launch of the next generation of hyperspectral satellite sensors in the next years, a high potential to meet the demand for global soil mapping and monitoring is appearing. In this paper, we briefly review the basic concepts of soil spectroscopy with a special attention to the effects of soil roughness on reflectance and then provide a review of state of the art, achievements and perspectives in soil mapping and monitoring based on imaging spectroscopy from air- and spaceborne sensors. Selected application cases are presented for the modelling of soil organic carbon, mineralogical composition, topsoil water content and characterization of soil crust, soil erosion and soil degradation stages based on airborne and simulated spaceborne imaging spectroscopy data. Further, current challenges, gaps and new directions toward enhanced soil properties modelling are presented. Overall, this paper highlights the potential and limitations of multiscale imaging spectroscopy nowadays for soil mapping and monitoring, and capabilities and requirements of upcoming spaceborne sensors as support for a more informed and sustainable use of our world’s soil resources.

Analysis of Obstacle and Soil Roughness Effects on the Wind Speed Profile by Using the Finite Element Method

Wind velocity variations near the ground correspond to turbulence effects, which are associated to shear occurring in the boundary layer due to the ground surface roughness and the presence of some obstacles. Computational fluid dynamics is used in this work in order to model these effects. The objective is to estimate the profile of the wind speed for various ground conditions with the aim to estimate more accurately the extractable energy from wind kinetic energy. A three-dimensional model describing the atmospheric circulations that are induced by the presence of an obstacle having the form of a hill and soil roughness was developed. The hill profile was schematized by a spherical surface and surface roughness by a scaled parameter. Appropriate boundary layer conditions were applied. Precision of the model was examined as function of the location of the boundary fixing the truncated computational domain of air and also the mesh size used. The boundary condition at the ground surface was fixed by a wall law. The obtained results have shown that the wind speed decreases when the obstacle height roughness increases. In addition to the main effect due to the presence of a hill, surface details such as bushes can significantly degrade the wind potential.

Global-scale surface roughness effects at L-band as estimated from SMOS observations

The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite dedicated to providing global surface soil moisture products. SMOS operates at L-band (1.4 GHz) and, at this frequency, the signal not only depends on soil moisture and vegetation optical depth but is also significantly affected by surface effects and, in particular, by the soil roughness. However, when dense vegetation is present, the L-band signal is poorly sensitive to the surface effects. First, by using multiple regressions between soil moisture (SM) and brightness temperature (TB) at different incidence angles and polarizations, the SMOS sensitivity to the surface effects was evaluated. A global-scale map of SMOS sensitivity to the surface effects was computed and showed that, for 87% of the land surface, the SMOS observations were sensitive to these effects, while very low sensitivity to the surface effects was estimated over 13% of the land surfaces. For instance, over broadleaf evergreen forest (mainly the Amazon and Congo forests), SMOS was sensitive to the surface effects over only half of the pixels considered. In a second step, in L-MEB (L-band Microwave Emission of the Biosphere), the forward emission model of the SMOS algorithm, the vegetation and roughness effects were combined in a single parameter, referred to as TR in this study. By inverting L-MEB, SM and TR were retrieved at global scale from the SMOS Level 3 (13) TB observations during 2011. Assuming a linear relationship between TR and the Leaf Area Index (LAI) obtained from MODIS data, the effects of roughness (H-r) and vegetation were decoupled and a global map of soil roughness effects was estimated. It was found that the spatial pattern of the H-r values could be related to the main vegetation types. Higher values of roughness (H-r = 0.32-0.39) were obtained for forests (broadleaf evergreen, deciduous and mixed coniferous) while lower values (H-r = 0.14-0.16) were obtained for deserts, shrubs and bare soils. Intermediate values (H-r = 0.20-0.23) were obtained over grasslands, tundra and cultivated land. Over vegetation biomes composed of forests and wooded grasslands, the H-r values were mainly correlated to the vegetation density (r similar to 0.55). For deserts, shrubs and bare soils, the H-r values were mainly correlated to the topography slopes (r similar to 0.53). The global maps presented in this study could lead to improved retrievals of soil moisture and vegetation optical depth for present and future microwave remote sensing missions such as SMOS and Soil Moisture Active Passive (SMAP).

Roughness and vegetation parameterizations at L-band for soil moisture retrievals over a vineyard field

Abstract The capability of L-band radiometry to monitor surface soil moisture (SM) at global scale has been analyzed in numerous studies, mostly in the framework of the ESA SMOS and NASA SMAP missions. To retrieve SM from L-band radiometric observations, two significant effects have to be accounted for, namely soil roughness and vegetation optical depth. In this study, soil roughness effects on retrieved SM values were evaluated using brightness temperatures acquired by the L-band ELBARA-II radiometer, over a vineyard field at the Valencia Anchor Station (VAS) site during the year 2013. Different combinations of the values of the model parameters used to account for soil roughness effects (HR, QR, NRH and NRV) in the L-MEB model were evaluated. The L-MEB model (L-band Microwave Emission of the Biosphere) is the forward radiative transfer model used in the SMOS soil moisture retrieval algorithm. In this model, HR parameterizes the intensity of roughness effects, QR accounts for polarization effects, and NRH and NRV parameterize the variations of the soil reflectivity as a function of the observation angle, θ, respectively for both H (Horizontal) and V (Vertical) polarizations. These evaluations were made by comparing in-situ measurements of SM (used here as a reference) against SM retrievals derived from tower-based ELBARA-II brightness temperatures mentioned above. The general retrieval approach consists of the inversion of L-MEB. Two specific configurations were tested: the classical 2-Parameter (2-P) retrieval configuration where SM and τNAD (vegetation optical depth at nadir) are retrieved, and a 3-Parameter (3-P) configuration, accounting for the additional effects of the vineyard vegetation structure. Using the 2-P configuration, it was found that setting NRp (p = H or V) equals to − 1 provided the best SM estimations in terms of correlation and unbiased Root Mean Square Error (ubRMSE). The assumption NRV = NRH = − 1 simplifies the L-MEB retrieval, since the two parameters τNAD and HR can then be grouped and retrieved as a single parameter (method here defined as the Simplified Retrieval Method (SRP)). The main advantage of the SRP method is that it is not necessary to calibrate HR before performing the SM retrievals. Using the 3-P configuration, the results improved, with respect to SM retrievals, in terms of correlation and ubRMSE, as the structural characteristics of the vineyards were better accounted for. However, this method still requires the calibration of HR, a disadvantage for operational applications. Finally, it was found that the use of in-situ roughness measurements to calibrate the roughness model parameters did not provide significant improvements in the SM retrievals as compared to the SRP method.

Effects of Soil Roughness on the Optimal Time of Cultivated Soils Observation by Satellites for the Soils Average Diurnal Albedo Approximation

This study explores the diurnal variation, from midday to sunset, in broadband blue-sky albedo (α) of soils with respect to their surface roughness. Five roughness levels of soil surfaces shaped by a plow, spike tooth harrow and roller, then also modified by rain, developed from the same soil material, were examined. The relation between α and the solar zenith angle enabled us to predict in which solar local time for chosen days of year the α of such surfaces, located between the latitude angles of 70° in the Northern and Southern Hemispheres, reaches its average diurnal value [(α)] . The usefulness of remote sensing satellite instruments for [(α)] approximation of the surfaces is evaluated here as the consequences of the difference between the optimal time for their [(α)] approximation and the time when the satellite instruments on the sun-synchronous orbits passing over the surfaces can observe them. Two examples of the orbits: close to that being optimal to the [(α)] approximation like NOAA-15, as well as that most often have been used to the α estimation such as MODIS instrument on the EOS Terra platform are considered.

A Simple Model of the Bare Soil Microwave Emission at L-Band

A simple reflectivity model of a bare soil at L-band is developed to account for the effects of soil roughness at different angles and polarizations. This model was developed using a long-term dataset acquired over the bare soil in the framework of the Surface Monitoring Of the Soil Reservoir EXperiment (SMOSREX). It is shown that the roughness effects are different depending on the measurement configuration, in terms of incidence angle and polarization. However, in this paper, a simple parameterization that is based on a single roughness parameter was calibrated in order to account for this angular and polarization dependencies. This parameter was found to be dependent on soil moisture: drier conditions were associated to higher ldquoroughnessrdquo conditions. The root-mean-square error between the measured and modeled reflectivities on days when no precipitation events were detected at vertical polarization (V-pol) is 0.0275, and at horizontal polarization (H-pol), the rmse is 0.0237; all incidence angles were considered. When all data are considered, the rmsd for V-pol is 0.0350, and for H-pol, the rmse is 0.0373. This new simple model is suitable for soil moisture retrieval from Soil Moisture and Ocean Salinity data. By means of this simple parameterization, almost two years of soil moisture data were retrieved with a good accuracy. The SMOSREX dataset allowed to ensure a long-term suitability of the proposed parameterization.

Effect of soil roughness on the inversion of off‐ground monostatic GPR signal for noninvasive quantification of soil properties

We report on a laboratory experiment that investigates the effect of soil surface roughness on the identification of the soil electromagnetic properties from full‐wave inversion of ground‐penetrating radar (GPR) data in the frequency domain. The GPR system consists of an ultrawide band stepped‐frequency continuous‐wave radar combined with an off‐ground monostatic horn antenna. Radar measurements were performed above a rectangular container filled with a loose sandy soil subject to seven water contents and four random surface roughnesses, including a smooth surface as reference. Compared to previous studies, we have reduced the modeling error of the GPR signal for the smooth surface case thanks to improved antenna transfer functions by solving an overdetermined system of equations based on six model configurations instead of only three. Then, the continuously increasing effect of surface roughness on the radar signal with respect to frequency is clearly observed. In close accordance with Rayleigh's criterion, both the radar signal and the inversely estimated parameters are not significantly affected if the surface protuberances are smaller than one eighth of a wavelength. In addition, when this criterion is not respected, errors are made in the estimated parameters, but the inverse solution remains stable. This demonstrates the promising perspectives for application of GPR for noninvasive water content estimation in agricultural and environmental field applications.

Quantitative estimation of skin soil moisture with the Special Sensor Microwave/Imager

A physically based retrieval scheme is described to infer near surface moisture content of bare soil patches from time series of the Special Sensor Microwave/Imager (SSM/I) Polarisation Difference Temperature (PDT) at 19.35 GHz. The PDT is modelled as a function of soil moisture using Fresnel's relations for specular reflection at the air-soil boundary, together with a dielectric mixing model for the moisture-dependent soil dielectric constant. The problem of the specification of parameters describing atmospheric and soil roughness effects is avoided by considering temporal variations of the PDT. The retrieval method is applied to the FIFE site for a two-week period in summer, during which heavy rainfall occurred on a dry upper soil. Although differences in sensing depths made a rigorous validation impossible, remotely sensed soil moisture showed a high degree of consistency with ground measurements. An error analysis shows that the method is capable of providing an accuracy of approximately 20% of moisture content at field capacity. Finally, it is demonstrated that the method is relatively insensitive to soil textural characteristics when moisture content is scaled with field capacity.

Relation between ERS‐1 synthetic aperture radar data and measurements of surface roughness and moisture content of rocky soils in a semiarid rangeland

Surface roughness and soil moisture content control the distribution of rainfall into runoff, evapotranspiration, and infiltration. Satellite radar data have the potential to provide spatial and multitemporal estimates of these variables, depending upon the sensor configuration and field condition. The relation between the European Remote Sensing Satellite (ERS‐1) synthetic aperture radar (SAR) data and measurements of surface roughness and moisture content of rocky soils in a semiarid rangeland in southeast Arizona was analyzed in this study. A dry and a wet season C band SAR image were acquired and corrected for topographic effects. Field soil roughness and moisture content data were obtained from 47 sampling sites. An intensive soil moisture sampling campaign was also conducted at three sites to determine the number of samples necessary to estimate soil moisture content with 10% accuracy. Dry and wet season SAR data were found to be correlated (r2=0.80 and 0.59, respectively) with root‐mean‐square (RMS) height measurements, while SAR data from the wet season image were poorly correlated with soil moisture. The results indicated that C band SAR data are promising for estimation of surface roughness in semiarid rangelands. However, they are less promising for soil moisture estimation, unless the effects of soil roughness and vegetation are removed. The acquisition of an adequate number of soil moisture samples to obtain representative soil moisture measurements is also a key issue in the validation of soil moisture retrieval from SAR data. In the study area, at least 17 samples per hectare were needed to obtain soil moisture estimates with 10% accuracy.

Taking into account vegetation effects to estimate soil moisture from C-band radar measurements

Estimation of surface soil moisture is one of the major potential applications of radar remote sensing. The European Remote Sensing Satellites (ERS 1 and 2) are equipped with a Synthetic Aperture Radar working at C-Band (5 Ghz) using a rather low incidence angle (23°). For this frequency and angle, the effect of soil roughness and vegetation attenuation are not negligible. Therefore, it is difficult to estimate the surface soil moisture using an algorithm that could be valid for the entire year. In this article it is shown, that, for wheat canopy, it is possible to apply an empirical relation for correcting for the effect of vegetation. The proposed algorithm is derived from a data set acquired over several years (from 1988 to 1994) using an airborne- radar. It uses a simple cloud model to describe the vegetation attenuation. This algorithm does not need very precise information on vegetation density and yields a final precision for the moisture content on the order of 0.05 cm 3/cm 3.

Estimating the characteristics of vegetation canopies with airborne radar measurements

Abstract Possible use of synthetic aperture radars (SAR) for monitoring agricultural canopies is investigated in this paper. Data have been acquired on the Orgcval watershed during the AGRISCATT'88 campaign. Four radar experiments were carried out with the airborne scattcrometer ERASME (C and X bands, HH and VV polarizations, multi-incidence angles). Simultaneous ground measurements (soil moisture, leaf area index, water content of the canopy) were conducted on 11 wheat fields. Backscattering coefficients of the canopies arc interpreted in the framework of semi-empirical ‘water-cloud’ models. A simple paramctrization of the angular effect of soil roughness is introduced, allowing the simultaneous use of multi-incidence angle radar data. With a unique set of parameters for each radar configuration ‘ frequency and polarization’ the water-cloud model appears to describe adequately the backscattering of all the fields, over the range of incidence angles. It is shown that in this case, attenuation is the domin...