2,835 research outputs found
Calculations of microwave brightness temperature of rough soil surfaces: Bare field
A model for simulating the brightness temperatures of soils with rough surfaces is developed. The surface emissivity of the soil media is obtained by the integration of the bistatic scattering coefficients for rough surfaces. The roughness of a soil surface is characterized by two parameters, the surface height standard deviation sigma and its horizontal correlation length l. The model calculations are compared to the measured angular variations of the polarized brightness temperatures at both 1.4 GHz and 5 GHz frequences. A nonlinear least-squares fitting method is used to obtain the values of delta and l that best characterize the surface roughness. The effect of shadowing is incorporated by introducing a function S(theta), which represents the probability that a point on a rough surface is not shadowed by other parts of the surface. The model results for the horizontal polarization are in excellent agreement with the data. However, for the vertical polarization, some discrepancies exist between the calculations and data, particularly at the 1.4 GHz frequency. Possible causes of the discrepancy are discussed
Coupled modelling of land surface microwave interactions using ENVISAT ASAR data
In the last decades microwave remote sensing has proven its capability to provide
valuable information about the land surface. New sensor generations as e.g.
ENVISAT ASAR are capable to provide frequent imagery with an high information
content. To make use of these multiple imaging capabilities, sophisticated
parameter inversion and assimilation strategies have to be applied. A profound
understanding of the microwave interactions at the land surface is therefore
essential.
The objective of the presented work is the analysis and quantitative description of
the backscattering processes of vegetated areas by means of microwave
backscattering models. The effect of changing imaging geometries is investigated
and models for the description of bare soil and vegetation backscattering are
developed. Spatially distributed model parameterisation is realized by synergistic
coupling of the microwave scattering models with a physically based land surface
process model. This enables the simulation of realistic SAR images, based on bioand
geophysical parameters.
The adequate preprocessing of the datasets is crucial for quantitative image
analysis. A stringent preprocessing and sophisticated terrain geocoding and
correction procedure is therefore suggested. It corrects the geometric and
radiometric distortions of the image products and is taken as the basis for further
analysis steps.
A problem in recently available microwave backscattering models is the inadequate
parameterisation of the surface roughness. It is shown, that the use of classical
roughness descriptors, as the rms height and autocorrelation length, will lead to
ambiguous model parameterisations. A new two parameter bare soil backscattering
model is therefore recommended to overcome this drawback. It is derived from
theoretical electromagnetic model simulations. The new bare soil surface scattering
model allows for the accurate description of the bare soil backscattering coefficients.
A new surface roughness parameter is introduced in this context, capable to
describe the surface roughness components, affecting the backscattering
coefficient. It is shown, that this parameter can be directly related to the intrinsic
fractal properties of the surface.
Spatially distributed information about the surface roughness is needed to derive
land surface parameters from SAR imagery. An algorithm for the derivation of the
new surface roughness parameter is therefore suggested. It is shown, that it can be
derived directly from multitemporal SAR imagery.
Starting from that point, the bare soil backscattering model is used to assess the
vegetation influence on the signal. By comparison of the residuals between
measured backscattering coefficients and those predicted by the bare soil
backscattering model, the vegetation influence on the signal can be quantified.
Significant difference between cereals (wheat and triticale) and maize is observed in
this context.
It is shown, that the vegetation influence on the signal can be directly derived from
alternating polarisation data for cereal fields. It is dependant on plant biophysical
variables as vegetation biomass and water content.
The backscattering behaviour of a maize stand is significantly different from that of
other cereals, due to its completely different density and shape of the plants. A
dihedral corner reflection between the soil and the stalk is identified as the major
source of backscattering from the vegetation. A semiempirical maize backscattering
model is suggested to quantify the influences of the canopy over the vegetation
period.
Thus, the different scattering contributions of the soil and vegetation components
are successfully separated. The combination of the bare soil and vegetation
backscattering models allows for the accurate prediction of the backscattering
coefficient for a wide range of surface conditions and variable incidence angles.
To enable the spatially distributed simulation of the SAR backscattering coefficient,
an interface to a process oriented land surface model is established, which provides
the necessary input variables for the backscattering model. Using this synergistic,
coupled modelling approach, a realistic simulation of SAR images becomes possible
based on land surface model output variables. It is shown, that this coupled
modelling approach leads to promising and accurate estimates of the backscattering
coefficients. The remaining residuals between simulated and measured backscatter
values are analysed to identify the sources of uncertainty in the model. A detailed
field based analysis of the simulation results revealed that imprecise soil moisture
predictions by the land surface model are a major source of uncertainty, which can
be related to imprecise soil texture distribution and soil hydrological properties.
The sensitivity of the backscattering coefficient to the soil moisture content of the
upper soil layer can be used to generate soil moisture maps from SAR imagery. An
algorithm for the inversion of soil moisture from the upper soil layer is suggested
and validated. It makes use of initial soil moisture values, provided by the land
surface process model. Soil moisture values are inverted by means of the coupled
land surface backscattering model. The retrieved soil moisture results have an RMSE
of 3.5 Vol %, which is comparable to the measurement accuracy of the reference
field data.
The developed models allow for the accurate prediction of the SAR backscattering
coefficient. The various soil and vegetation scattering contributions can be
separated. The direct interface to a physically based land surface process model
allows for the spatially distributed modelling of the backscattering coefficient and
the direct assimilation of remote sensing data into a land surface process model.
The developed models allow for the derivation of static and dynamic landsurface
parameters, as e.g. surface roughness, soil texture, soil moisture and biomass from
remote sensing data and their assimilation in process models. They are therefore
reliable tools, which can be used for sophisticated practice oriented problem
solutions in manifold manner in the earth and environmental sciences
Influence of Radar Frequency on the Relationship Between Bare Surface Soil Moisture Vertical Profile and Radar Backscatter
International audienceThe aim of this letter is to discuss the influence of radar frequency on the relationship between surface soil moisture and the nature of radar backscatter over bare soils. In an attempt to address this issue, the advanced integral equation model was used to simulate backscatter from soil surfaces with various moisture vertical profiles, for three frequency bands, namely, L, C, and X. In these computations, we investigated the influence of the vertical heterogeneity of soil moisture on the characteristics of the backscattered signals. The influence of radar frequency is clearly demonstrated. A database produced from Envisat ASAR and TerraSAR-X data, which was acquired over bare soils with in situ measurements of moisture content and ground surface roughness, was used to validate the utility of taking the soil moisture heterogeneity into account in the backscatter model
A newsoil roughness parameter for themodelling of radar backscattering over bare soil
International audienceThe characterisation of soil surface roughness is a key requirement for the correct analysis of radar backscattering behaviour. It is noteworthy that an increase in the number of surface roughness parameters in a model also increases the difficulty with which data can be inverted for the purposes of estimating soil parameters. In this paper, a new description of soil surface roughness is proposed for microwave applications. This is based on an original roughness parameter, Zg, which combines the three most commonly used soil parameters: root mean surface height, correlation length, and correlation function shape, into just one parameter. Numerical modelling, based on the moment method and integral equations, is used to evaluate the relevance of this approach. It is applied over a broad dataset of numerically generated surfaces characterised by a large range of surface roughness parameters. A strong correlation is observed between this new parameter and the radar backscattering simulations, for the HH and VV polarisations in the C and X bands. It is proposed to validate this approach using data acquired in the C and X bands, at several agricultural sites in France. It was found that the parameter Zg has a high potential for the analysis of surface roughness using radar measurements. An empirical model is proposed for the simulation of backscattered radar signals over bare soil
Influence of Surface Roughness Spatial Variability and Temporal Dynamics on the Retrieval of Soil Moisture from SAR Observations
Radar-based surface soil moisture retrieval has been subject of intense research during the last decades. However, several difficulties hamper the operational estimation of soil moisture based on currently available spaceborne sensors. The main difficulty experienced so far results from the strong influence of other surface characteristics, mainly roughness, on the backscattering coefficient, which hinders the soil moisture inversion. This is especially true for single configuration observations where the solution to the surface backscattering problem is ill-posed. Over agricultural areas cultivated with winter cereal crops, roughness can be assumed to remain constant along the growing cycle allowing the use of simplified approaches that facilitate the estimation of the moisture content of soils. However, the field scale spatial variability and temporal variations of roughness can introduce errors in the estimation of soil moisture that are difficult to evaluate. The objective of this study is to assess the impact of roughness spatial variability and roughness temporal variations on the retrieval of soil moisture from radar observations. A series of laser profilometer measurements were performed over several fields in an experimental watershed from September 2004 to March 2005. The influence of the observed roughness variability and its temporal variations on the retrieval of soil moisture is studied using simulations performed with the Integral Equation Model, considering different sensor configurations. Results show that both field scale roughness spatial variability and its temporal variations are aspects that need to be taken into account, since they can introduce large errors on the retrieved soil moisture values
Semi-empirical calibration of the Integral Equation Model for SAR data in C-band and cross polarization using radar images and field measurements
The estimation of surface soil parameters (moisture and roughness) from Synthetic Aperture Radar (SAR) images requires the use of well-calibrated backscattering models. The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed by Baghdadi et al. (2004 and 2006) for HH and VV polarizations to HV polarization. The approach consisted in replacing the measured correlation length by a fitting/calibration parameter so that model simulations would closely agree with radar measurements. This calibration in C-band covers radar configurations with incidence angles between 24° and 45.8°. Good agreement was found between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM
Quantitative Estimation of Surface Soil Moisture in Agricultural Landscapes using Spaceborne Synthetic Aperture Radar Imaging at Different Frequencies and Polarizations
Soil moisture and its distribution in space and time plays an important role in the surface energy balance at the soil-atmosphere interface. It is a key variable influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Due to their large spatial variability, estimation of spatial patterns of soil moisture from field measurements is difficult and not feasible for large scale analyses. In the past decades, Synthetic Aperture Radar (SAR) remote sensing has proven its potential to quantitatively estimate near surface soil moisture at high spatial resolutions. Since the knowledge of the basic SAR concepts is important to understand the impact of different natural terrain features on the quantitative estimation of soil moisture and other surface parameters, the fundamental principles of synthetic aperture radar imaging are discussed. Also the two spaceborne SAR missions whose data was used in this study, the ENVISAT of the European Space Agency (ESA) and the ALOS of the Japanese Aerospace Exploration Agency (JAXA), are introduced. Subsequently, the two essential surface properties in the field of radar remote sensing, surface soil moisture and surface roughness are defined, and the established methods of their measurement are described. The in situ data used in this study, as well as the research area, the River Rur catchment, with the individual test sites where the data was collected between 2007 and 2010, are specified. On this basis, the important scattering theories in radar polarimetry are discussed and their application is demonstrated using novel polarimetric ALOS/PALSAR data. A critical review of different classical approaches to invert soil moisture from SAR imaging is provided. Five prevalent models have been chosen with the aim to provide an overview of the evolution of ideas and techniques in the field of soil moisture estimation from active microwave data. As the core of this work, a new semi-empirical model for the inversion of surface soil moisture from dual polarimetric L-band SAR data is introduced. This novel approach utilizes advanced polarimetric decomposition techniques to correct for the disturbing effects from surface roughness and vegetation on the soil moisture retrieval without the use of a priori knowledge. The land use specific algorithms for bare soil, grassland, sugar beet, and winter wheat allow quantitative estimations with accuracies in the order of 4 Vol.-%. Application of remotely sensed soil moisture patterns is demonstrated on the basis of mesoscale SAR data by investigating the variability of soil moisture patterns at different spatial scales ranging from field scale to catchment scale. The results show that the variability of surface soil moisture decreases with increasing wetness states at all scales. Finally, the conclusions from this dissertational research are summarized and future perspectives on how to extend the proposed model by means of improved ground based measurements and upcoming advances in sensor technology are discussed. The results obtained in this thesis lead to the conclusion that state-of-the-art spaceborne dual polarimetric L-band SAR systems are not only suitable to accurately retrieve surface soil moisture contents of bare as well as of vegetated agricultural fields and grassland, but for the first time also allow investigating within-field spatial heterogeneities from space
Estimation of Soil Moisture Using Active Microwave Remote Sensing
The method for developing a soil moisture inversion algorithm using Radar data can be approached in two ways: the multiple-incident angle approach and the change detection method. This thesis discusses how these two methods can be used to predict surface soil moisture. In the multiple incident angle approach, surface roughness can be mapped, if multiple incident angle viewing is possible and if the surface roughness is assumed constant during data acquisitions. A backpropagation neural network (NN) is trained with the data set generated by the Integral Equation Method (IEM) model. The training data set includes possible combinations of backscatter obtained as a result of variation in dielectric constant within the period of data acquisitions. The inputs to the network are backscatter acquired at different incident angles. The outputs are correlation length and root mean square height (rms). Once the roughness is mapped using these outputs, dielectric constant can be determined. Three different data sets, (backscatter acquired from multiplerequencies, multiple-polarizations, and multiple-incident angles) are used to train the NN. The performance of the NN trained by the different data sets is compared. The next approach is the application of the change detection concept. In this approach, the relative change in dielectric constant over two different periods is determined from Radarsat data using a simplified algorithm. The vegetation backscatter contribution can be removed with the aid of multi-spectral data provided by Landsat. A method is proposed that minimizes the effect of incident angle on Radar backscatter by normalizing the acquired SAR images to a reference angle. A quantitative comparison of some of the existing soil moisture estimation algorithms is also mad
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