Sensitivity to soil moisture by active and passive microwavesensors

The backscatter measured by radar and the emission measured by a radiometer are both very sensitive to the moisture content m_υ of bare-soil surfaces. Vegetation cover complicates the scattering and emission processes, and it has been presumed that the addition of vegetation masks the soil surface, thereby reducing the radiometric and radar soil-moisture sensitivities. Even though researchers working in the field of microwave remote sensing of soil moisture are all likely to agree with the preceding two statements, numerous claims and counterclaims have been voiced, primarily at symposia and workshops, espousing the superiority of the radiometric technique over the radar, or vice versa. The discussion is often reduced to disagreements over the answer to the following question “Which of the two sensing techniques is less impacted by vegetation cover?” This paper is an attempt to answer that question. Using realistic radiative-transfer models for the emission and backscatter, calculations were performed for three types of canopies, all at 1.5 GHz. The results lead to two major conclusions. First, the accepted presumption that vegetation cover reduces the soil-moisture sensitivity is not always true. Over certain ranges of the optical depth τ of the vegetation canopy and the roughness of the soil surface, vegetation cover can enhance, not reduce, the radar sensitivity to soil moisture. The second conclusion is that under most vegetation and soil-surface conditions, the radiometric and radar soil-moisture sensitivities decrease with increasing τ, and the rates are approximately the same for both sensors, suggesting that at least as far as vegetation effects are concerned, neither sensor can claim superiority over the other     

High-resolution change estimation of soil moisture using L-band radiometer and Radar observations made during the SMEX02 experiments

The soil moisture experiments held during June-July 2002 (SMEX02) at Iowa demonstrated the potential of the L-band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L-band radar was also shown to be sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite L-band radiometer is of the order of tens of kilometers, which is not sufficient to serve the full range of science needs for land surface hydrology and weather modeling applications. Disaggregation schemes for deriving subpixel estimates of soil moisture from radiometer data using higher resolution radar observations may provide the means for making available global soil moisture observations at a much finer scale. This paper presents a simple approach for estimation of change in soil moisture at a higher (radar) spatial resolution by combining L-band copolarized radar backscattering coefficients and L-band radiometric brightness temperatures. Sensitivity of AIRSAR L-band copolarized channels has been demonstrated by comparison with in situ soil moisture measurements as well as PALS brightness temperatures. The change estimation algorithm has been applied to coincident PALS and AIRSAR datasets acquired during the SMEX02 campaign. Using AIRSAR data aggregated to a 100-m resolution, PALS radiometer estimates of soil moisture change at a 400-m resolution have been disaggregated to 100-m resolution. The effect of surface roughness variability on the change estimation algorithm has been explained using integral equation model (IEM) simulations. A simulation experiment using synthetic data has been performed to analyze the performance of the algorithm over a region undergoing gradual wetting and dry down.     

Microwave backscattering and emission model for grass canopies

Microwave radar and radiometer measurements of grasslands indicate a substantial reduction in sensor sensitivity to soil moisture in the presence of a thatch layer. When this layer is wet it masks changes in the underlying soil, making the canopy appear warm in the case of passive sensors (radiometer) and decreasing backscatter in the active case (scatterometer). A model for a grass canopy with thatch is presented in order to explain this behavior and for comparison with observations. The canopy model consists of three layers: grass, thatch, and the underlying soil. The grass blades are modeled by elongated elliptical discs and the thatch is modeled as a collection of disk shaped water droplets (i.e., the dry matter is neglected). The ground is homogeneous and flat. The distorted Born approximation is used to compute the radar cross section of this three layer canopy and the emissivity is computed from the radar cross section using the Peake formulation for the passive problem. Results are computed at L-band (1.4 GHz) and C-band (4.75 GHz) using canopy parameters (i.e., plant geometry, soil moisture, plant moisture, etc.) representative of Konza Prairie grasslands. The results are compared to C-band scatterometer measurements and L-band radiometer measurements at these grasslands     

L-band radiometer measurements of soil water under growing clover grass

A field experiment with an L-band radiometer at 1.4 GHz was performed from May-July 2004 at an experimental site near Zurich, Switzerland. Before the experiment started, clover grass was seeded. Thermal infrared, in situ temperature, and time-domain reflectometer (TDR) measurements were taken simultaneously with hourly radiometer measurements. This setup allowed for investigation of the microwave optical depths and mode opacities (parallel and perpendicular to the soil surface) of the clover grass canopy. Optical depths and opacities were determined by in situ analysis and remotely sensed measurements using a nonscattering radiative transfer model. Due to the canopy structure, optical depth and opacity depend on the polarization and radiometer direction, respectively. A linear relation between vegetation water-mass equivalent and polarization-averaged optical depth was observed. Furthermore, measured and modeled radiative transfer properties of the canopy were compared. The model is based on an effective-medium approach considering the vegetation components as ellipsoidal inclusions. The effect of the canopy structure on the opacities was simulated by assuming an anisotropic orientation of the vegetation components. The observed effect of modified canopy structure due to a hail event was successfully reproduced by the model. It is demonstrated that anisotropic vegetation models should be used to represent the emission properties of vegetation. The sensitivity of radiometer measurements to soil water content was investigated in terms of the fractional contribution of radiation emitted from the soil to total radiation. The fraction of soil-emitted radiation was reduced to approximately 0.3 at the most developed vegetation state. The results presented contribute toward a better understanding of the interaction between L-band radiation and vegetation canopies. Such knowledge is important for evaluating data generated from future satellite measurements.     

SMOS REFLEX 2003: L-band emissivity characterization of vineyards

The goal of the Soil Moisture and Ocean Salinity mission over land is to infer surface soil moisture from multiangular L-band radiometric measurements. As the canopy affects the microwave emission of land, it is necessary to characterize different vegetation layers. This paper presents the Reference Pixel L-Band Experiment (REFLEX), carried out in June-July 2003 at the Vale/spl grave/ncia Anchor Station, Spain, to study the effects of grapevines on the soil emission and on the soil moisture retrieval. A wide range of soil moisture (SM), from saturated to completely dry soil, was measured with the Universitat Polite/spl grave/cnica de Catalunya's L-band Automatic Radiometer (LAURA). Concurrently with the radiometric measurements, the gravimetric soil moisture, temperature, and roughness were measured, and the vines were fully characterized. The opacity and albedo of the vineyard have been estimated and found to be independent on the polarization. The /spl tau/--/spl omega/ model has been used to retrieve the SM and the vegetation parameters, obtaining a good accuracy for incidence angles up to 55/spl deg/. Algorithms with a three-parameter optimization (SM, albedo albedo, and opacity) exhibit a better performance than those with one-parameter optimization (SM).     

Parameter sensitivity of soil moisture retrievals from airborne L-band radiometer measurements in SMEX02

Over the past two decades, successful estimation of soil moisture has been accomplished using L-band microwave radiometer data. However, remaining uncertainties related to surface roughness and the absorption, scattering, and emission by vegetation must be resolved before soil moisture retrieval algorithms can be applied with known and acceptable accuracy using satellite observations. Surface characteristics are highly variable in space and time, and there has been little effort made to determine the parameter estimation accuracies required to meet a given soil moisture retrieval accuracy specification. This study quantifies the sensitivities of soil moisture retrieved using an L-band single-polarization algorithm to three land surface parameters for corn and soybean sites in Iowa, United States. Model sensitivity to the input parameters was found to be much greater when soil moisture is high. For even moderately wet soils, extremely high sensitivity of retrieved soil moisture to some model parameters for corn and soybeans caused the retrievals to be unstable. Parameter accuracies required for consistent estimation of soil moisture in mixed agricultural areas within retrieval algorithm specifications are estimated. Given the spatial and temporal variability of vegetation and soil conditions for agricultural regions it seems unlikely that, for the single-frequency, single-polarization retrieval algorithm used in this analysis, the parameter accuracy requirements can be met with current satellite-based land surface products. We conclude that for regions with substantial vegetation, particularly where the vegetation is changing rapidly, any soil moisture retrieval algorithm that is based on the physics and parameterizations used in this study will require multiple frequencies, polarizations, or look angles to produce stable, reliable soil moisture estimates.     

Discrete scatter model for microwave radar and radiometer responseto corn: comparison of theory and data

As part of the Multisensor Aircraft Campaign, MACHYDRO, two microwave sensors, NASA's Airborne Synthetic Aperture Radar (AIRSAR) and Pushbroom Microwave Radiometer (PBMR) collected data over the same corn fields during the summer of 1990. During these flights, measurements were made on the ground of soil moisture and plant parameters. In this paper the measured canopy and soil parameters are used in a discrete scatter model to predict the response of both sensors (radar and radiometer). A distorted Born approximation is used to compute the scattering coefficient for the corn canopy. The backscatter coefficient gives the radar response and the radiometer response is obtained by integrating the bistatic coefficient over all scattering angles above ground. The objective of this analysis is to test the model and, in particular, to determine how well a single set of plant parameters and single model can yield agreement with both the radar and radiometer measurements. The model values are in reasonably good agreement with the measurements at horizontal polarization and reflect observed changes in soil moisture     

A multifrequency algorithm for the retrieval of soil moisture on alarge scale using microwave data from SMMR and SSM/I satellites

The sensitivity of microwave emission at different frequencies to soil moisture in bare and vegetated soils has been investigated using experimental data. Since the best frequency for the measurement of soil moisture (L-band) is absent in current satellite sensors, it is necessary to seek alternative solutions. An algorithm is proposed for the retrieval of soil moisture based on the sensitivity to moisture of both the brightness temperature and the polarization index at C-band, one that is able to correct for the effect of vegetation by means of the polarization index at X-band. The algorithm has been tested by using experimental data collected with airborne microwave radiometers on agricultural areas and validated by using the data sets of special sensor microwave/imager (SMM/I) and scanning multichannel microwave radiometer (SMMR). These research activities are planned in view of coming new satellites: AQUA (NASA) and ADEOS-II (NASDA), which will be launched by the end of 2001. These will have new generation microwave radiometers (AMSR-E and AMSR) onboard, which show much better characteristics with respect to the previous sensors, in particular an enhanced spatial resolution     

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 method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and Radar observations

NASA's Earth System Science Pathfinder Hydrospheric States (Hydros) mission will provide the first global scale space-borne observations of Earth's soil moisture using both L-band microwave radiometer and radar technologies. In preparation for the Hydros mission, an observation system simulation experiment (OSSE) has been conducted. As a part of this OSSE, the potential for retrieving useful surface soil moisture at spatial resolutions of 9 and 3 km was explored. The approach involved optimally merging relatively accurate 36-km radiometer brightness temperature and relatively noisy 3-km radar backscatter cross section observations using a Bayesian method. Based on the Hydros OSSE data sets with low and high noises added to the simulated observations or model parameters, the Bayesian method performed better than direct inversion of either the brightness temperature or radar backscatter observations alone. The root-mean-square errors of 9-km soil moisture retrievals from the Bayesian merging method were reduced by 0.5 %vol/vol and 1.4 %vol/vol from the errors of direct radar inversions for the entire OSSE domain of all 34 consecutive days for the low and high noise data sets, respectively. Improvement in soil moisture estimates using the Bayesian merging method over the direct inversions of radar or radiometer data were even more significant for soil moisture retrieval at 3-km resolution. However, to address the representativeness of these results at the global and multiyear scales, further performance comparison studies are needed, particularly with actual field data.     

The SIR-B Observations of Microwave Backscatter Dependence on Soil Moisture, Surface Roughness, and Vegetation Covers

An experiment was conducted from an L-band syntheticaperture perture radar aboard space shuttle Challenger in October 1984 to study the microwave backscatter dependence on soil moisture, surface roughness, and vegetation cover. The results based on the anlyses of an image obtained at 21° incidence angle show a positive correlation between scattering coefficient and soil moisture content, with a sensitivity comparable to that derived from the ground radar measurements [1]. The surface roughness strongly affects the microwave backscatter. A factor of 2 change in the standard deviation of surface roughness height gives a corresponding change of about 8 dB in the scattering coefficient. The microwave backscatter also depends on the vegetation types. Under the dry soil conditions, the scattering coefficient is observed to change from about -24 dB for an alfalfa or lettuce field to about -17 dB for a mature corn field. These results suggest that observations with a synthetic-aperture radar system of multiple frequencies ies and polarizations are required to unravel the effects of soil ture,oisre, surface roughness, and vegetation cover.     

Attenuation of soil microwave emission by corn and soybeans at 1.4and 5 GHz

Theory and experiments have shown that passive microwave radiometers can be used to measure soil moisture. However, the presence of a vegetative cover alters the measurement that might be obtained under bare conditions. Two significant obstacles to the practical use of this approach are deterministically accounting for the effect of vegetation; and developing algorithms for extracting soil moisture from observations of a vegetation-soil complex. The presence of a vegetation canopy reduces the sensitivity of passive microwave instruments to soil moisture variations. Data collected using truck-mounted microwave radiometers were used to examine the specific effects of corn and soybean canopies     

A semi-empirical backscattering model at L-band and C-band for asoybean 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° in elevation and to 45° 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 ²=0.867 and a root mean square error (RMSE) of 0.0678 kg/m 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 R²=0.898 and volumetric soil moisture RMSE of 1.75%     

Galactic noise and passive microwave remote sensing from space at L-band

The spectral window at L-band (1.413 GHz) is important for passive remote sensing of soil moisture and ocean salinity from space, parameters that are needed to understand the hydrological cycle and ocean circulation. At this frequency, radiation from celestial (mostly Galactic) sources is strong and, unlike the constant cosmic background, this radiation is spatially variable. This paper presents a modern radiometric map of the celestial sky at L-band and a solution for the problem of determining what portion of the sky is seen by a down-looking radiometer in orbit. The data for the radiometric map are derived from recent radio astronomy surveys and are presented as equivalent brightness temperature suitable for remote sensing applications. Examples using orbits and antennas representative of those contemplated for remote sensing of soil moisture and sea surface salinity from space are presented to illustrate the signal levels to be expected. Near the Galactic plane, the contribution can exceed several kelvin.     

Characterizing the dependence of vegetation model parameters on crop structure, incidence angle, and polarization at L-band

To retrieve soil moisture over vegetation-covered areas from microwave radiometry, it is necessary to account for vegetation effects. At L-band, many retrieval approaches are based on a simple model that relies on two vegetation parameters: the optical depth (/spl tau/) and the single-scattering albedo (/spl omega/). When the retrievals are based on multiconfiguration measurements, it is necessary to take into account the dependence of /spl tau/ and /spl omega/ on the system configuration, in terms of incidence angle and polarization. In this paper, this dependence was investigated for several crop types (corn, soybean, wheat, grass, and alfalfa) based on L-band experimental datasets. The results should be useful for developing more accurate forward modeling and retrieval methods over mixed pixels including a variety of vegetation types.     

The b-factor as a function of frequency and canopy type at H-polarization

For anticipated synergistic approaches of the L-band radiometer on the Soil Moisture and Ocean Salinity (SMOS) mission with higher frequency microwave radiometers such as the Advanced Microwave Scanning Radiometer (AMSR) (C-band), a reanalysis has been performed on the frequency dependence of the linear relationship between vegetation optical depth (/spl tau//sub o/) and vegetation water content (W), given by /spl tau//sub o/=b/spl middot/W. Insight into the frequency dependence of the b-factor is important for the retrieval of surface moisture from dual- or multifrequency microwave brightness temperature observations from space over vegetation-covered regions using model inversion techniques. The b-values presented in the literature are based on different methods and approaches. Therefore, a direct comparison is not straightforward and requires a critical analysis. This paper confirms that when a large frequency domain is considered, the b-factor is inversely proportional to the power of the wavelength b=c/(/spl lambda/)/sup x/, which is in line with theoretical considerations. It was found that different canopy types could be separated into different groups, each with a different combination of values for log(c) and x, which characterize the linearized relationship log(b)=log(c)-x/spl middot/log(/spl lambda/). A comparison of ratios b/sub C//b/sub L/ (with C and L denoting C- and L-band, respectively) also resulted in basically the same groups.     

Microwave Emission from Row Crops

In order to examine the emission properties of vegetation without taking into consideration the effects of variations in the soil background, strips of metal screening were used to cover the soil surface between adjacent rows of plants. Temporal measurements were made at 2.7 and 5.1 GHz for soybean, wheat, and corn canopies. Several special experiments were conducted to evaluate the sensitivity of brightness temperature to look direction (relative to row direction), polarization configuration, and incidence angle, and to evaluate the emission contributions of defoliated stalks. In general, the results show that the canopy is highly anisotropic, the emission exhibits a strong dependence on polarization and look direction, and the scattering albedo is typically less than 0.1. Canopy transmissivity was estimated from the radiometric observations and then related empirically to the canopy's integrated water content. Using this relation in a zero-order radiative transfer model led to good agreement between the experimental observations and the model predictions.     

Utilization of Active Microwave Roughness Measurements to Improve Passive Microwave Soil Moisture Estimates Over Bare Soils

Investigators have researched operational microwave techniques for the remote estimation of soil moisture for sometime now. Both active and passive microwave sensors respond to variations in soil moisture, but also respond to vegetation and roughness parameters. This has led to research in multisensor techniques which account for the interference. Previously, techniques have been developed which used visible and infrared bands (similar to Landsat) to compensate for the vegetation masking on the L-band passive radiometer's response to soil moisture. In contrast, this study compensates for the surface roughness effect by using microwave scatterometer data on the same L-band radiometer. It was found that the L-band radiometer's capability to estimate soil moisture over bare fields was significantly improved when surface roughness was accounted for with scatterometers.     

Global Simulation of Brightness Temperatures at 6.6 and 10.7 GHz Over Land Based on SMMR Data Set Analysis

In the framework of the Soil Moisture and Ocean Salinity mission, a two-year (1987–1988) global simulation of brightness temperatures (TB) at L-band was performed using a simple model [L-band microwave emission of the biosphere, (L-MEB)] based on radiative transfer equations. However, the lack of alternative L-band spaceborne measurements corresponding to real-world data prevented from assessing the realism of the simulated global-scale TB fields. In this study, using a similar modeling approach, TB simulations were performed at C-band and X-band. These simulations required the development of C-MEB and X-MEB models, corresponding to the equivalent of L-MEB at C-band and X-band, respectively. These simulations were compared with Scanning Multichannel Microwave Radiometer (SMMR) measurements during the period January to August 1987 (corresponding to the end of life of the SMMR mission). A sensitivity study was also carried out to assess, at a global scale, the relative contributions of the main MEB parameters (particularly the roughness and vegetation model parameters). Regional differences between simulated and measured TBs were analyzed, discriminating possible issues either linked to the radiative transfer model (C-MEB and X-MEB) or due to land surface simulations. A global agreement between observations and simulations was discussed and allowed to evaluate regions where soil moisture retrievals would give best results. This comparison step made at C-band and X-band allowed to better assess how realistic and/or accurate the L-band simulations could be.     

Ku- and C-band SAR for discriminating agricultural crop and soilconditions

A method is proposed to estimate both green leaf area index (GLAI) and soil moisture (h_v), based on radar measurements at the Ku-band (14.85 GHz) and C-band (5.35 GHz) frequencies. The Ku-band backscatter at large incidence angles was found to be independent of soil moisture conditions and could be used alone to estimate GLAI. Then, the Ku-band estimate of GLAI could be used with a measurement of C-band backscatter in a canopy radiative transfer model to isolate the value of h_v. This concept was demonstrated with a set of Kuand C-band synthetic aperture radar (SAR) backscatter data acquired over agricultural fields in Arizona. The demonstration showed promise for operational application of the method, though several limitations were identified. Since both Ku- and C-band σ_° are sensitive to soil roughness, this approach must be applied only to fields of similar soil roughness or row direction. This limitation may be less serious for farm management applications since crop type and cultivation practices are generally well known and can be taken into consideration. Another limitation of the use of Ku- and C-band σ° is the apparent saturation of the Ku-band signal with increasing GLAI. Operational implementation of this approach will require dual-frequency sensors aboard an aircraft or orbiting satellite