Semi-empirical Backscattering Model Research Articles

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

The aim of this paper is to analyze the potential of X-band SAR measurements (COSMO-SkyMed and TerraSAR-X) made over bare soils for the estimation of soil moisture and surface geometry parameters at a semi-arid site in Tunisia (North Africa). Radar signals acquired with different configurations (HH and VV polarizations, incidence angles of 26° and 36°) are statistically compared with ground measurements (soil moisture and roughness parameters). The radar measurements are found to be highly sensitive to the various soil parameters of interest. A linear relationship is determined for the radar signals as a function of volumetric soil moisture, and a logarithmic correlation is observed between the radar signals and three surface roughness parameters: the root mean square height (Hrms), the parameter Zs = Hrms2/l (where l is the correlation length) and the parameter Zg = Hrms × (Hrms/l)α (where α is the power of the surface height correlation function). The highest dynamic sensitivity is observed for Zg at high incidence angles. Finally, the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models) is compared with SAR measurements. The results provide an indication of the limits of validity of the IEM and Dubois models, for various radar configurations and roughness conditions. Considerable improvements in the IEM model performance are observed using the Baghdadi-calibrated version of this model.

Leaf area index estimation of lowland rice using semi-empirical backscattering model

Rice crop monitoring using synthetic aperture radar (SAR) polarimetry is one of the thrust areas of research in radar remote sensing due to unavailability of optical data in critical growth stages of crop because of dense cloud cover during the growing season (monsoon) in India and Southeast Asia. The prime objective of this study was to assess the potential of polari- metric C-band SAR data to estimate the leaf area index (LAI) of two varieties (non-basmati and basmati) of paddy rice. Seven fine Quad-pol single look complex (SLC) RADARSAT-2 data were acquired over part of the Indo-Gangetic plain, India, in 2011 and 2012, and ground data were collected during the same period of the satellite pass. The backscatter of rice in differ- ent polarizations over different growth periods was analyzed and LAI estimated using a semi- empirical model. The performance of the model was evaluated by statistical parameters, namely. root mean square error and coefficient of determination (R 2 ). The model performed well for the LAI estimation of non-basmati rice using HV backscatter while achieving acceptable accuracy for others. Results of this study provided the promising approach for LAI prediction using SAR data in India. © 2013 Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/1.JRS.7

Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data

The aim of this study was to estimate soil moisture from RADARSAT-2 Synthetic Aperture Radar (SAR) images acquired over agricultural fields. The adopted approach is based on the combination of semi-empirical backscattering models, four RADARSAT-2 images and coincident ground measurements (soil moisture, soil surface roughness and vegetation characteristics) obtained near Saskatoon, Saskatchewan, Canada during the summer of 2008. The depolarization ratio (χ v ), the co-polarized correlation coefficient ( ρ vvhh ) and the ratio of the absolute value of cross polarization to crop height (Λ vh ) derived from RADARSAT-2 data were analyzed with respect to changes in soil surface roughness, crop height, soil moisture and vegetation water content. This sensitivity analysis allowed us to develop empirical relationships for soil surface roughness, crop height and crop water content estimation regardless of crop type. The latter were then used to correct the semi-empirical Water–Cloud model for soil surface roughness and vegetation effects in order to retrieve soil moisture data. The soil moisture retrieved algorithm is evaluated over mature crop fields (wheat, pea, lentil, and canola) using ground measurements. Results show average relative errors of 19%, 10%, 25.5% and 32% respectively for the retrieval of crop height, soil surface roughness, crop water content and soil moisture.

Assimilation of ASAR data with a hydrologic and semi-empirical backscattering coupled model to estimate soil moisture

The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model—Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.

Evaluation of empirical and semi-empirical backscattering models for surface soil moisture estimation

Several empirical and semi-empirical backscattering models have been proposed to offer alternative expressions for the inversion of surface parameters from radar data, but the applicability and adequacy of the models for different surface conditions and sensor configurations have not been clearly assessed. A number of empirical and semi-empirical models are studied in this paper to assess the applicability of the models for different conditions that are often found over agricultural areas. The performance of the models is evaluated first analytically by comparing their simulations with those obtained using the theoretical integral equation model (IEM) and geometrical optics model (GOM). The model estimations are then compared with RADARSAT-1 observations acquired over an experimental catchment. The results show very different model behaviour depending on the surfaces roughness conditions and incidence angle. This study highlights the importance of carefully selecting the backscattering model to be used in radar applications.

Analysis of Surface Roughness Heterogeneity and Scattering Behavior for Radar Measurements

The use of a theoretical backscatter model to analyze medium to low spatial resolution microwave data is still very complicated, particularly because of the difficulty in defining a unique roughness parameter, capable of adequately representing heterogeneous terrain. In this paper, an approach is proposed for roughness analysis and the modeling of backscattering, under conditions of surface heterogeneity. This paper is based on the use of a semiempirical backscattering model, defined with a single roughness parameter Zs=s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /l (s being the root mean square surface height and l the correlation length). The proposed backscattering model has been validated with integral equation model simulations, for high radar incidence angles, and within its domain of roughness validity. A range of experimental measurements was used to validate the model expressions. The effective low spatial resolution roughness, inferred from signals backscattered from a surface of heterogeneous roughness, is defined for different roughness classes

A semiempirical surface backscattering model for bare soil surfaces based on a generalized power law spectrum approach

An adequate characterization of surface roughness is crucial to obtain reliable backscatter simulation results from existing analytical backscattering models. The surface roughness is typically characterized using root mean square height, autocorrelation length, and shape of the autocorrelation function. For the solution of inverse problems it is of interest to reduce the number of unknown surface parameters. Simplified backscattering models are required in this context. The paper introduces a new semiempirical backscattering model in C-band for rough dielectric surfaces which is based on the integral equation model. It is shown that a surface roughness description can be reduced using a single surface roughness parameter. To account for the high variability of autocorrelation function types, the proposed model is based on a generalized power law spectrum approach which mediates between Gaussian and exponential correlated surfaces. The approach is validated against analytical backscatter simulations and laboratory-measured microwave signatures, and the surface parameter retrieval capabilities of the suggested model are investigated.

OBSERVATIONS OF L-AND C-BAND BACKSCATTER AND A SEMI-EMPIRICAL BACKSCATTERING MODEL APPROACH FROM A FOREST-SNOW-GROUND SYSTEM

Observations of Land C-band backscatter from snow cover are presented at all polarizations. Airborne passive-microwave data was collected in Northern Finland during EMAC’95 (European Multi-sensor Airborne Campaign-95). The measurements cover the 6.8–18.7 GHz frequency range with both vertical and horizontal polarizations. The empirical SAR data were acquired by EMISAR of Technical University of Denmark over the city of Oulu in Northern Finland during EMAC’95. Airborne measurements were conducted on 22–23 March, and on 2–3 May 1995. The land-use map of the test sites was obtained from the National Land Survey of Finland. This study combines the semi-empirical and empirical models that were developed. Applicability of the forest transmissivity formulas developed by using the different data sets of passive and active sensors is shown. Because of the effect of dry snow at C-band is more visible than at L-band. A C-band semi-empirical backscattering model is developed for the forest-snow-ground system.

A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion

Radar backscatter measurements of a pair of adjacent soybean fields at L-band and C-band are reported. These measurements, which are fully polarimetric, took place over the entire growing season of 1996. To reduce the data acquisition burden, these measurements were restricted to 45/spl deg/ in elevation and to 45/spl deg/ in azimuth with respect to the row direction. Using the first order radiative transfer solution as a form for the model of the data, four parameters were extracted from the data for each frequency/polarization channel to provide a least squares fit to the model. For inversion, particular channel combinations were regressed against the soil moisture and area density of vegetation water mass. Using L-band cross-polarization and VV-polarization, the vegetation water mass can be regressed with an R/sup 2/=0.867 and a root mean square error (RMSE) of 0.0678 kg/m/sup 2/. Similarly, while a number of channels, or combinations of channels, can be used to invert for soil moisture, the best combination observed, namely, L-band VV-polarization, C-band HV- and VV-polarizations, can achieve a regression coefficient of R/sup 2/=0.898 and volumetric soil moisture RMSE of 1.75%.

The seasonal behavior of interferometric coherence in boreal forest

The capability of SAR interferometry has been previously demonstrated in various applications. In particular, the use of interferometric coherence has shown promising results in forest monitoring, however, mainly in discriminating forested and nonforested areas. The authors have collected ERS-1 and ERS-2 Tandem data from two boreal forest test sites in Finland. The data have been processed into interferometric coherence and intensity images. These images have been used to a) compare the behavior of interferometric coherence and intensity for various land-use and forest classes and b) extensive analysis on the behavior of interferometric coherence in boreal forests as a function of stem volume. Based on the observations and the use of a boreal forest semi-empirical backscattering model, they have developed an empirical model that describes interferometric coherence of boreal forests using backscattering information. The results indicate that coherence is more sensitive to the stem volume than the C-band backscattering intensity. However, the intensity and coherence data contain complementary information and therefore, the use of both data sources is beneficial in the observation of boreal forest.

Thermodielectric effects on radar backscattering from wet soil

Thermodielectric interactions between free and bound soil water at different soil textures and frequencies affect radar backscatter. Simulations with a semi-empirical backscatter model show a potential for temperature corrections in inference of water content. A new thermodielectric response backscatter difference index was derived for remote mapping of surface soil texture.

Seasonal comparison of HUTSCAT ranging scatterometer and ERS-1 SAR microwave signatures of boreal forest zone

A set of ERS-1 SAR images along with airborne non-imaging ranging scatterometer (HUTSCAT) measurements and in situ surveys has been obtained from the Sodankyla test site (center latitude=67.41/spl deg/N, center longitude=26.58/spl deg/E) in Northern Finland. A total of five measurement campaigns were organized during 1991-1993. Nineteen test lines have been selected from the test site to represent different land-use categories. The land-use in the test area consists of open areas (agricultural fields, bogs, and clear-cut areas) and sparsely forested areas (mires, pine, and mixed forests). Microwave signatures representing the test lines have been extracted from ERS-1 SAR images and HUTSCAT measurements. The behavior of these signatures has been compared with each other and with their boreal forest semiempirical backscattering model. A set of extensive field measurements (snow depth, density, wetness, coverage, and snow water equivalent) made on the test lines are used in the various analyzes. The results indicate that the behavior of ERS-1 SAR microwave signatures is similar to that of HUTSCAT even in the presence of forest canopies. Also, the deviations of microwave signatures for various land-use classes behave similarly. This allows the authors to use the boreal forest semiempirical backscattering model based on HUTSCAT data to divide the ERS-1 backscattering signal into two contributions: 1) backscattering contribution from the top layer of vegetation canopy and 2) backscattering contribution from the canopy, ground, and ground-canopy reflections. By using the boreal forest semiempirical model, the behavior of these contributions is also observed in various soil conditions. These results explain some aspects of the boreal forest backscattering mechanism in the presence of snow cover and wet soil, which have not been experimentally investigated before.

Retrieval of biomass in boreal forests from multitemporal ERS-1 and JERS-1 SAR images

The response of JERS-1 and ERS-1 synthetic aperture radar (SAR) to the forest stem volume (biomass) was investigated by employing a digital stem volume map and weather information. The stem volume map was produced from the National Forest Inventory sample plot data together with a LANDSAT thematic mapper (TM) image. A new indirect inversion method was developed and tested to estimate the forest blockwise stem volume from JERS-1 and/or ERS-1 SAR images. The method is based on using a semiempirical backscatter model for inversion. The model presumes that backscatter from a forest canopy is determined by the stem volume, soil moisture, and vegetation moisture. The area of interest is divided into a training and test area. In this study, the training area was 10% of the test site, while the remaining 90% was used for testing the method. The inversion algorithm is carried out in the following three steps. 1) For the training area, the soil and vegetation moisture parameters are estimated from the backscattering coefficients and stem volume (must be known for training areas) with the semiempirical backscatter model. 2) For the area of interest, the stem volume is estimated from the moisture parameters and backscattering coefficients with the semiempirical backscattering model. 3) If several SAR images are used, the stem volume estimates are combined with a multiple linear regression. The regression equation is defined using the stem volume estimates for the training area. The results for the stem volume estimation using L-band and/or C-band SAR data showed promising accuracies: the relative retrieval rms error varied from 30 to 5% as the size of the forest area varied from 5 to 30000 ha.

Observations and Interpretation of Seasonal ERS-1 Wind Scatterometer Data over Northern Sahel (Mali)

This article presents an analysis of ERS-1 wind-scatterometer (WSC) data acquired over a region located in the northern Sahel. The considered period extends from January 1992 to December 1995, that is, covering four vegetation cycles. Experimental observations show that WSC responses at 45° of incidence angle display a marked seasonality associated with the development and senescence of annual grasses during the successive rainy seasons. The interpretation of the σ° temporal plots is performed with the assistance of a semiempirical backscattering model combined with an ecosystem grassland model. Contributions of the various components of the Sahelian landscape to the total backscattering are identified. Overall, the soil contribution is always large but the σ°(45) temporal plots reflect well the vegetation development. The analysis of the different contributions leads to a simplification of the backscattering model. Finally, this latter is parameterized as a function of two surface parameters, namely, the soil volumetric water content and the vegetation biomass. This simplified model allows the vegetation biomass to be estimated with a 33% error.

Seasonal dynamics of C-band backscatter of boreal forests with applications to biomass and soil moisture estimation

The seasonal changes of the C-band backscattering properties of boreal forests are investigated by applying 1) a semiempirical forest backscattering model and 2) multitemporal ERS-1 SAR data from two test areas in Finland. The semiempirical modeling of forest canopy volume backscattering and extinction properties is based on high-resolution data from the authors' ranging scatterometer HUTSCAT. The response of ERS-1 SAR to forest stem volume (biomass) and other forest characteristics is investigated by employing the National Forest Inventory sample plots, stand-wise forest inventory data and LANDSAT- and SPOT-based digital land use maps. The results show that the correlation between the backscattering coefficient and forest stem volume (biomass) varies from positive to negative depending on canopy and soil moisture. Additionally, the seasonal snow cover and soil freezing/thawing effects cause drastic changes in the radar response. A novel method for the estimation of forest stem volume (biomass) is introduced. This technique is based on the use of: 1) multitemporal ERS-1 SAR data; 2) reference sample plot information; and 3) the semiempirical backscattering model. It is shown that the multitemporal ERS-1 SAR images can be successfully used for estimating the forest stem volume. The effects of soil moisture variations to ERS-1 SAR results have been analyzed using hydrological soil moisture model and in situ data. The results indicate that the semiempirical model can he used for predicting the soil and canopy moisture variations in ERS-1 images.

C-band backscatter signatures of old sea ice in the central Arctic during freeze-up

Radar backscatter signatures of old sea ice in the central Arctic have been measured and analyzed. A ship-mounted scatterometer was used to acquire backscattering coefficients at 5.4 GHz in the four linear polarization states and at incidence angles between 20 degrees and 60 degrees . Detailed in situ characterizations of the snow and ice were also made to enable comparison with theoretical backscatter models. Freeze-up conditions were prevalent during the experiment. The average backscattering coefficient was found to increase when the temperature of the ice surface layer decreased. The semi-empirical backscatter model is used to evaluate the measurements and shows that the backscatter increase is due to an increasing penetration depth, causing the volume scattering to increase. Model predictions also show that both surface and volume scattering contribute significantly at incidence angles of 20 degrees to 26 degrees . At these incidence angles, the dominating scattering mechanism changes from surface to volume scattering as the ice surface temperature decreases.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>