M.C. Dobson

Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models

This paper is the second in a series evaluating the microwave dielectric behavior of soil-water mixtures as a function of water content and soil textural composition. Part II draws upon the data presented in Part 1 [13] to develop appropriate empirical and theoretical dielectric mixing models for the 1.4-to 18-GHz region. A semiempirical mixing model based upon the index of refraction is presented, requiring only easily ascertained soil physical parameters such as volumetric moisture and soil textural composition as inputs. In addition, a theoretical model accounting explicitly for the presence of a hydration layer of bound water adjacent to hydrophilic soil particle surfaces is presented. A four-component dielectric mixing model treats the soil-water system as a host medium of dry soil solids containing randomly distributed and randomly oriented disc-shaped inclusions of bound water, bulk water, and air. The bulk water component is considered to be dependent upon frequency, temperature, and salinity. The soil solution is differentiated by means of a soil physical model into 1) a bound component and 2) a bulk soil solution. The performance of each model is evaluated as a function of soil moisture, soil texture, and frequency, using the dielectric measurements of five soils ranging from sandy loam to silty clay (as presented in Part I [13]) at frequencies between 1.4 and 18 GHz. The semiempirical mixing model yields an excellent fit to the measured data at frequencies above 4 GHz. At 1.

Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations

This is the first paper in a two-part sequence that evaluates the microwave dielectric behavior of soil-water mixtures as a function of water content, temperature, and soil textural composition. Part I presents the results of dielectric constant measurements conducted for five different soil types at frequencies between 1.4 and 18 GHz. Soil texture is shown to have an effect on dielectric behavior over the entire frequency range and is most pronounced at frequencies below 5 GHz. In addition, the dielectric properties of frozen soils suggest that a fraction of the soil water component remains liquid even at temperatures of -24° C. The dielectric data as measured at room temperature are summarized at each frequency by polynomial expressions dependent upon both the volumetric moisture content m and the percentage of sand and clay contained in the soil; separate polynomial expressions are given for the real and imaginary parts of the dielectric constant. In Part II, two dielectric mixing models will be presented to account for the observed behavior: 1) a semiempirical refractive mixing model that accurately describes the data and requires only volumetric moisture and soil texture as inputs, and 2) a theoretical four-component mixing model that explicitly accounts for the presence of bound water.

Dependence of radar backscatter on coniferous forest biomass

Two independent experimental efforts have examined the dependence of radar backscatter on above-ground biomass of monospecie conifer forests using polarimetric airborne SAR data at P-, L- and C-bands. Plantations of maritime pines near Landes, France, range in age from 8 to 46 years with above-ground biomass between 5 and 105 tons/ha. Loblolly pine stands established on abandoned agricultural fields near Duke, NC, range in age from 4 to 90 years and extend the range of above-ground biomass to 560 tons/ha for the older stands. These two experimental forests are largely complementary with respect to biomass. Radar backscatter is found to increase approximately linearly with increasing biomass until it saturates at a biomass level that depends on the radar frequency. The biomass saturation level is about 200 tons/ha at P-band and 100 tons/ha at L-band, and the C-band backscattering coefficient shows much less sensitivity to total above-ground biomass. >

Microwave Backscatter Dependence on Surface Roughness, Soil Moisture, and Soil Texture: Part I-Bare Soil

This is the first in a series of two papers on the use of active microwave remote sensing for measuring the moisture content of bare (Part I) and vegetation-covered (Part II) soil. An experimental program was conducted to evaluate the response of the backscattering coefficient to soil moisture content as a means to specify radar system parameters for future airborne and/or spaceborne soil moisture mappers. Particular attention was paid to the effects of surface roughness, and a preliminary examination of the role of soil texture was performed. The results of this investigation confirm the findings of a previous experiment [1] which concluded that the effects of surface roughness can be minimized by operating at a frequency in the neighborhood of 5 GHz over the 7-17° angle of incidence range. The precision with which soil moisture in the surface soil layer can be estimated is comparable to the precision of the ground-truthed estimate. Because the moisture in the surface layer is highly correlated to the subsurface moisture, it was not possible to determine experimentally the effective depth of the layer responsible for the observed radar backscatter.

Dielectric properties of soils in the 0.3-1.3-GHz range

In 1985, the authors reported the development of a semiempirical dielectric model for soils, covering the frequency range between 1.4 and 18 GHz. The model provides expressions for the real and imaginary parts of the relative dielectric constant of a soil medium in terms of the soils textural composition (sand, silt, and clay fractions), the bulk density and volumetric moisture content of the soil, and the dielectric constant of water at the specified microwave frequency and physical temperature. This communication provides similar expressions for the 0.3-1.3-GHz range. Upon comparing experimental results measured in this study with predictions based on the semiempirical model, it was found that the model underpredicts the real part of the dielectric constant for high-moisture cases and underestimates the imaginary part for all soils and moisture conditions. A small linear adjustment has been introduced to correct the expression for the real part and a new equation was generated for the effective conductivity to correct the expression for the imaginary part. In addition, dielectric measurements were made to evaluate the dependence of the dielectric constant on clay type. The results show significant variations for the real part and large variations for the imaginary part among soils with the same clay fractions but with clays of different specific surface areas. >

Estimation of forest biophysical characteristics in Northern Michigan with SIR-C/X-SAR

A three-step process is presented for estimation of forest biophysical properties from orbital polarimetric SAR data. Simple direct retrieval of total aboveground biomass is shown to be ill-posed unless the effects of forest structure are explicitly taken into account. The process first involves classification by (1) using SAR data to classify terrain on the basis of structural categories or (2) a priori classification of vegetation type on some other basis. Next, polarimetric SAR data at L- and C-bands are used to estimate basal area, height and dry crown biomass for forested areas. The estimation algorithms are empirically determined and are specific to each structural class. The last step uses a simple biophysical model to combine the estimates of basal area and height with ancillary information on trunk taper factor and wood density to estimate trunk biomass. Total biomass is estimated as the sum of crown and trunk biomass. The methodology is tested using SIR-C data obtained from the Raco Supersite in Northern Michigan on Apr. 15, 1994. This site is located at the ecotone between the boreal forest and northern temperate forests, and includes forest communities common to both. The results show that for the forest communities examined, biophysical attributes can be estimated with relatively small rms errors: (1) height (0-23 m) with rms error of 2.4 m, (2) basal area (0-72 m/sup 2//ha) with rms error of 3.5 m/sup 2//ha, (3) dry trunk biomass (0-19 kg/m/sup 2/) with rms error of 1.1 kg/m/sup 2/, (4) dry crown biomass (0-6 kg/m/sup 2/) with rms error of 0.5 kg/m/sup 2/, and (5) total aboveground biomass (0-25 kg/m/sup 2/) with rms error of 1.4 kg/m/sup 2/. The addition of X-SAR data to SIR-C was found to yield substantial further improvement in estimates of crown biomass in particular. However, due to a small sample size resulting from antenna misalignment between SIR-C and X-SAR, the statistical significance of this improvement cannot be reliably established until further data are analyzed. Finally, the results reported are for a small subset of the data acquired by SIR-C/X-SAR. >

Microwave Backscatter Dependence on Surface Roughness, Soil Moisture, and Soil Texture: Part II-Vegetation-Covered Soil

Results are presented of an experimental investigation to determine the relationship between radar backscatter coefficient ¿° and soil moisture for vegetation-covered soil. These results extend a previous report which showed the experimental relationship between ¿° and soil moisture for bare soil [1]. It is shown that the highest correlation between ¿° and soil moisture is 0.92 for the combined response of four crop types measured at 4.25 GHz, 10° incidence angle, and HH polarization. Radar look direction, relative to the crop row direction, is shown to have an insignificant effect on soil moisture estimation if the radar frequency is higher than 4 GHz. The dependence on soil type can be minimized by expressing soil moisture in units of percent of field capacity. The possibility of using a single radar for measuring soil moisture for both bare and vegetated fields is demonstrated with a linear estimation algorithm having an experimental correlation coefficient of 0.8.

Knowledge-based land-cover classification using ERS-1/JERS-1 SAR composites

Land-cover classification of an ERS-1/JERS-1 composite is explored in the context of regional- to global-scale applicability. Each of these orbiting synthetic aperture radars provide somewhat complementary information since data is collected using significantly different frequencies, polarizations, and look angles (ERS-1: C-band, VV polarization, 23/spl deg/; JERS-1: L-band, HH polarization, 35/spl deg/). This results in a classification procedure for the composite image (a co-registered pair from the same season) that is superior to that obtained from either of the two sensors alone. A conceptual model is presented to show how simple structural attributes of terrain surfaces and vegetation cover relate to the data from these two sensors. The conceptual model is knowledge based; and it is supported by both theoretical considerations and experimental observations. The knowledge-based, conceptual model is incorporated into a classifier that uses hierarchical decision rules to differentiate land-cover classes. The land-cover classes are defined on the basis of generalized structural properties of widespread applicability. The classifier operates sequentially and produces two levels of classification. At level-2, terrain is structurally differentiated into man-made features (urban), surfaces, short vegetation, and tall vegetation. At level-2, the tall vegetation class is differentiated on the basis of plant architectural properties of the woody stems and foliage. Growth forms of woody stems include excurrent (i.e., pines), decurrent (i.e., oaks), and columnar (i.e., palm) architecture. Two classes of leaves are considered: broadleaf and needle-leaf. The composite classifier yields overall accuracies in excess of 90% for a test site in northern Michigan located along the southern ecotone of the boreal forest. For the area examined, the SAR-based classification is superior to unsupervised classification of multitemporal AVHRR data supplemented with a priori information on elevation, climate, and ecoregion.

Effects of Vegetation Cover on the Microwave Radiometric Sensitivity to Soil Moisture

The reduction in sensitivity of the microwave brightness temperature to soil moisture content due to vegetation cover is analyzed using airborne observations made at 1.4 and 5 GHz. The data were acquired during six flights in 1978 over a test site near Colby, Kansas. The test site consisted of bare soil, wheat stubble, and fully mature corn fields. The results for corn indicate that the radiometric sensitivity to soil moisture S decreases in magnitude with increasing frequency and with increasing angle of incidence (relative to nadir).The sensitivity reduction factor, defined in terms of the radiometric sensitivities for bare soil and canopy-covered conditions Y=1 - Scan/ Ss was found to be equal to 0.65 for normal incidence at 1.4 GHz, and increases to 0.89 at 5 GHz. These results confirm previous conclusions that the presence of vegetation cover may pose a serious problem for soil moisture detection with passive microwave sensors.

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%.