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.     

Estimation of bare surface soil moisture and surface roughnessparameter using L-band SAR image data

An algorithm based on a fit of the single-scattering integral equation method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by the Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well-managed watershed in southwest Oklahoma. Prior to its application for soil moisture inversion, a good agreement was found between the single-scattering IEM simulations and the L-band measurements of SIR-C and AIRSAR over a wide range of soil moisture and surface roughness conditions. The sensitivity of soil moisture variation to the co-polarized signals were then examined under the consideration of the calibration accuracy of various components of SAR measurements. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively     

Passive active L- and S-band (PALS) microwave sensor for oceansalinity and soil moisture measurements

A passive/active WS-band (PALS) microwave aircraft instrument to measure ocean salinity and soil moisture has been built and tested. Because the L-band brightness temperatures associated with salinity changes are expected to be small, it was necessary to build a very sensitive and stable system. This new instrument has dual-frequency, dual polarization radiometer and radar sensors. The antenna is a high beam efficiency conical horn. The PALS instrument was installed on the NCAR C-130 aircraft and soil moisture measurements were made in support of the Southern Great Plains 1999 experiment in Oklahoma from July 8-14, 1999. Data taken before and after a rainstorm showed significant changes in the brightness temperatures, polarization ratios and radar backscatter, as a function of soil moisture. Salinity measurement missions were flown on July 17-19, 1999, southeast of Norfolk, VA, over the Gulf Stream. The measurements indicated a clear and repeatable salinity signal during these three days, which was in good agreement with the Cape Hatteras ship salinity data. Data were also taken in the open ocean and a small decrease of 0.2 K was measured in the brightness temperature, which corresponded to the salinity increase of 0.4 psu measured by the M/V Oleander vessel     

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.     

Subpixel variability of remotely sensed soil moisture: aninter-comparison study of SAR and ESTAR

The representation of subpixel variability in soil moisture estimates from passive microwave data was investigated through sensitivity analysis and by comparison against the spatial structure of soil moisture fields derived from radar data. This work shows that the subpixel variability not represented in brightness temperature fields is directly associated with the spatial organization of soil hydraulic properties and the spatial distribution of vegetation. The significant implication of this result is that the physical connection between soil moisture estimates at the pixel scale and local values within the pixel weakens strongly as the sensor resolution decreases. Subsequently, the application of scaling and fractal interpolation principles to downscale passive microwave data to the spatial resolution of radar data was investigated as a means to recover spatial structure. In particular, ESTAR soil moisture data was successfully downscaled from 200 to 40 m using only one radar frequency (e.g., L-band). This application suggests that the combined use of active and passive single-band microwave remote-sensing of soil moisture is a viable approach to improve the spatial resolution of soil moisture remote-sensing     

Improvement of Moisture Estimation Accuracy of Vegetation-Covered Soil by Combined Active/Passive Microwave Remote Sensing

The measured effects of vegetation canopies on radar and radiometric sensitivity to soil moisture are compared to first-order emission and scattering models. The models are found to predict the measured emission and backscattering with reasonable accuracy for various crop canopies at frequencies between 1.4 and 5.0 GHz, especially at angles of incidence less than 30°. The vegetation loss factor L (?) increases with frequency and is found to be dependent upon canopy type and water content. In addition, the effective radiometric power absorption coefficient of a mature corn canopy is roughly 1.75 times that calculated for the radar at the same frequency. Comparison of an L-band radiometer with a C-band radar shows the two systems to be complementary in terms of accurate soil moisture sensing over the extreme range of naturally occurring soil-moisture conditions. The combination of both an L-band radiometer and a C-band radar is expected to yield soil-moisture estimates that are accurate to better than +/-30 percent of true soil moisture, even for a soil under a lossy crop canopy such as mature corn. This is true even without any other ancillary information.     

The hydrosphere State (hydros) Satellite mission: an Earth system pathfinder for global mapping of soil moisture and land freeze/thaw

The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Administration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earth's soil moisture at 10-km resolution with two- to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture. The mission data will enable advances in weather and climate prediction and in mapping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39/spl deg/ with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented.     

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.     

Tests of sequential data assimilation for retrieving profile soilmoisture and temperature from observed L-band radiobrightness

Sequential data assimilation (Kalman filter optimal estimation) techniques are applied to the problem of retrieving near-surface soil moisture and temperature state from periodic terrestrial radiobrightness observations that update soil heat and moisture diffusion models. The retrieval procedure uses a time-explicit numerical model to continuously propagate the soil state profile, its error of estimation, and its interdepth covariances through time. The model's coupled soil moisture and heat fluxes are constrained by micrometeorology boundary conditions drawn from observations or atmospheric modeling. When radiometer data are available, the Kalman filter updates the state profile estimate by weighing the propagated state, error, and covariance estimates against an a priori estimate of radiometric measurement error. The Kalman filter compares predicted and observed radiobrightnesses directly, so no inverse algorithm relating brightness to physical parameters is required. The authors demonstrate Kalman filter model effectiveness using field observations and a simulation study. An observed 1 m soil state profile is recovered over an eight-day period from daily L-band observations following an intentionally poor initial state estimate. In a four-month simulation study, they gauge the longer term behavior of the soil state retrieval and Kalman gain through multiple rain events, soil dry-downs, and updates from radiobrightnesses     

A Simple Method to Disaggregate Passive Microwave-Based Soil Moisture

This paper develops two alternative approaches for downscaling passive microwave-derived soil moisture. Ground and airborne data collected over the Walnut Gulch experimental watershed during the Monsoon'90 experiment were used to test these approaches. These data consisted of eight micrometeorological stations (METFLUX) and six flights of the L-band Push Broom Microwave Radiometer (PBMR). For each PBMR flight, the 180-m resolution L-band pixels covering the eight METFLUX sites were first aggregated to generate a 500-m ldquocoarse-scalerdquo passive microwave pixel. The coarse-scale-derived soil moisture was then downscaled to the 180-m resolution using two different surface soil moisture indexes (SMIs): (1) the evaporative fraction (EF), which is the ratio of the evapotranspiration to the total energy available at the surface; and (2) the actual EF (AEF), which is defined as the ratio of the actual-to-potential evapotranspiration. It is well known that both SMIs depend on the surface soil moisture. However, they are also influenced by other factors such as vegetation cover, soil type, root-zone soil moisture, and atmospheric conditions. In order to decouple the influence of soil moisture from the other factors, a land surface model was used to account for the heterogeneity of vegetation cover, soil type, and atmospheric conditions. The overall accuracy in the downscaled values was evaluated to 3% (vol.) for EF and 2% (vol.) for AEF under cloud-free conditions. These results illustrate the potential use of satellite-based estimates of instantaneous evapotranspiration on clear-sky days for downscaling the coarse-resolution passive microwave 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     

Multispectral Remote Sensing of Saline Seeps

An aircraft experiment was conducted in early summer of 1981 to determine the feasibility of optical and microwave remote sensing techniques for the detection of fully developed and incipient saline seeps in South Dakota and Montana. The NASA C-130 earth resources aircraft was used to acquire L-band and C-band scatterometer data (backscattering coefficient profiles), radiometer data (brightness temperature profiles), and color-infrared photography; additional passive microwave data and thermal images were acquired by the L- band radiometer on the Beechcraft D-18 aircraft operated by South Dakota State University. Intensive soil moisture and salinity data were collected on a uniform 20-m grid spacing and at several depths for the 600 × 600 m South Dakota site. The two Montana sites were over-flown with flight lines several kilometers in length, and ground truth information was obtained by identifying known geological and geohydrological units with varying soil salinities on a regional basis. The C-130 radiometers (both L- and C-bands) were effective in detecting wet soil areas including fully developed seeps; however, incipient seeps were not accurately detected by the radiometers. The D-18 L-band radiometer data did not appear to be sensitive to soil wetness. The C-130 scatterometer data profiles, although showing some sensitivity to soil moisture, were greatly influenced by surface roughness and appear to be ineffective in accurately delineating either fully developed or incipient seeps. Thermal-IR scanner data acquired by the D-18 aircraft did not appear to provide a reliable means for identifying potential seeps.     

Variational data assimilation of microwave radiobrightnessobservations for land surface hydrology applications

Our ability to accurately describe large-scale variations in soil moisture is severely restricted by process uncertainty and the limited availability of appropriate soil moisture data. Remotely sensed microwave radiobrightness observations can cover large scales but have limited resolution and are only indirectly related to the hydrologic variables of interest. The authors describe a four-dimensional (4D) variational assimilation algorithm that makes best use of available information while accounting for both measurement and model uncertainty. The representer method used is more efficient than a Kalman filter because it avoids explicit propagation of state error covariances. In a synthetic example, which is based on a field experiment, the authors demonstrate estimation performance by examining data residuals. Such tests provide a convenient way to check the statistical assumptions of the approach and to assess its operational feasibility. Internally computed covariances show that the estimation error decreases with increasing soil moisture. An adjoint analysis reveals that trends in model errors in the soil moisture equation can be estimated from daily L-band brightness measurements, whereas model errors in the soil and canopy temperature equations cannot be adequately retrieved from daily data alone. Nonetheless, state estimates obtained from the assimilation algorithm improve significantly on prior model predictions derived without assimilation of radiobrightness data     

Statistical characteristics of simulated radar imagery from baresoil surfaces: effects of surface roughness and soil moisturevariability

The potential of high-resolution radar imagery to estimate various hydrological parameters, such as soil moisture, has long been recognized. Image simulation is one approach to study the interrelationships between the radar response and the underlying ground parameters. In order to perform realistic simulations, the authors incorporated the effects of naturally occurring spatial variability and spatial correlations of those ground parameters that affect the radar response, primarily surface roughness and soil moisture. Surface roughness and soil moisture images were generated for a hypothetical 100×100 m bare soil surface area at 1 m resolution using valid probability distributions and correlation lengths. These values were then used to obtain copolarized radar scattering coefficients at 2 GHz (L band) and 10 GHz (X band) frequencies using appropriate backscatter models, which were then converted to a digital number within 0-255 gray scale in order to generate radar images. The effect of surface roughness variability causes variability in the radar image, which is more apparent under smooth soil conditions. On the other hand, the inherent spatial pattern in soil moisture tends to cause similar patterns in the radar image under rougher soil conditions. The maximum difference between contrast-enhanced mean values of the radar image digital number due to moisture variations occurs at surface roughness values in the 1.5-2.0 cm range     

Effect of multiple scattering on the phase signature of wet subsurface structures: applications to polarimetric L- and C-band SAR

We propose a two-layer integral equation model (IEM) model including multiple-scattering terms to reproduce the phase signature of buried wet structures that we observed on L-band synthetic aperture radar (SAR) images. We have good agreement between the extended (single+multiple scattering) IEM model and previous results obtained using a single-scattering IEM model combined with finite-difference time-domain simulations. We show that the multiple scattering not only significantly influences the copolarized phase difference but can also be related to the soil moisture content. In order to assess the validity of our extended model, we performed radar measurements on a natural outdoor site and showed that they could be fairly well fitted to the extended model. A parametric analysis presents the dependence of the copolarized phase difference on roughness parameters (rms height and correlation length) and radar parameters (frequency and incidence angle). Our study also shows that the phase signature should allow detection of buried wet structures down to a larger depth for C-band (3.8 m) than for L-band (2.6 m). This signature could then be used to map subsurface moisture in arid regions using polarimetric SAR systems.     

Measuring soil moisture with imaging radars

An empirical algorithm for the retrieval of soil moisture content and surface root mean square (RMS) height from remotely sensed radar data was developed using scatterometer data. The algorithm is optimized for bare surfaces and requires two copolarized channels at a frequency between 1.5 and 11 GHz. It gives best results for kh⩽2.5, μ_υ⩽35%, and &thetas;⩾30°. Omitting the usually weaker hv-polarized returns makes the algorithm less sensitive to system cross-talk and system noise, simplifies the calibration process and adds robustness to the algorithm in the presence of vegetation. However, inversion results indicate that significant amounts of vegetation (NDVI>0.4) cause the algorithm to underestimate soil moisture and overestimate RMS height. A simple criteria based on the σ_hv⁰/σ_vv⁰ ratio is developed to select the areas where the inversion is not impaired by the vegetation. The inversion accuracy is assessed on the original scatterometer data sets but also on several SAR data sets by comparing the derived soil moisture values with in-situ measurements collected over a variety of scenes between 1991 and 1994. Both spaceborne (SIR-C) and airborne (AIRSAR) data are used in the test. Over this large sample of conditions, the RMS error in the soil moisture estimate is found to be less than 4.2% soil moisture     

L波段微波辐射计周期脉冲式干扰时域检测方法研究

L波段微波辐射计是探测土壤湿度和海水盐度的有效遥感器。但是,全球定位系统(GPS)信号、雷达信号以及一些商用电子产品的电磁辐射造成的频谱污染都可以对微波辐射计的探测造成干扰,使得被动微波遥感对地观测结果具有一定的偏差,降低了地表参数的反演精度。该文通过实验模拟脉冲式噪声干扰,观测其在L波段(全功率接收型式)微波辐射计系统中的传输特性,分析输出信号特性与辐射计参数(积分时间、灵敏度)的相关性,获取其数字特征参数,结合脉冲检测法(APB),提出一种新的自相关检测(ACD)算法,能够有效用于周期性的脉冲式辐射干扰的检测,在微波辐射计系统积分时间1 ms的情况下,能够检测1.5 K的噪声干扰,满足卫星遥感探测反演地表参数精度的需求。     

Soil moisture retrieval from space: the Soil Moisture and OceanSalinity (SMOS) mission

Microwave radiometry at low frequencies (L-band: 1.4 GHz, 21 cm) is an established technique for estimating surface soil moisture and sea surface salinity with a suitable sensitivity. However, from space, large antennas (several meters) are required to achieve an adequate spatial resolution at L-band. So as to reduce the problem of putting into orbit a large filled antenna, the possibility of using antenna synthesis methods has been investigated. Such a system, relying on a deployable structure, has now proved to be feasible and has led to the Soil Moisture and Ocean Salinity (SMOS) mission, which is described. The main objective of the SMOS mission is to deliver key variables of the land surfaces (soil moisture fields), and of ocean surfaces (sea surface salinity fields). The SMOS mission is based on a dual polarized L-band radiometer using aperture synthesis (two-dimensional [2D] interferometer) so as to achieve a ground resolution of 50 km at the swath edges coupled with multiangular acquisitions. The radiometer will enable frequent and global coverage of the globe and deliver surface soil moisture fields over land and sea surface salinity over the oceans. The SMOS mission was proposed to the European Space Agency (ESA) in the framework of the Earth Explorer Opportunity Missions. It was selected for a tentative launch in 2005. The goal of this paper is to present the main aspects of the baseline mission and describe how soil moisture will be retrieved from SMOS data     

Microwave L-band emission of freezing soil

We report on field-measured microwave emission in a period of frost penetration into a grassland soil. The measurements were recorded with a high temporal resolution using an L-band radiometer mounted on a 7-m high tower. The observation period (December 2002 to March 2003) included two cycles of soil freezing and thawing with maximum frost depth of 25 cm. In situ soil temperature and liquid water content were measured at five depths down to 45 cm. Soil moisture profiles were calculated using the COUP numerical soil water and heat model in combination with measured soil properties and meteorological data monitored at the site. The L-band radiation data clearly showed the penetration and thawing of seasonal soil frost. We calculated soil reflectivities based on in situ measured and modeled soil moisture profiles by applying a coherent radiative transfer model. The calculated reflectivities were compared with the radiometrically determined soil reflectivities. It was demonstrated that the quantitative consistency between these reflectivities was significantly improved by applying an impedance matching approach accounting for surface effects. In this particular case, the dielectric structure of the uppermost soil horizon was largely influenced by soil roughness, vegetation, and snow cover. The radiometrically measured soil reflectivities were fitted using a radiative transfer model in combination with a roughness model assuming a soil surface roughness of 25 mm. The analysis during a period of frost penetration shows coherent behavior of the soil reflectivity. Temporal oscillation of the measured L-band radiation appears to be a coherent effect. This effect has the potential to be used for estimating the frost penetration velocity.     

一种L波段相控阵微波辐射计射频干扰检测算法

目前,微波辐射计均面临严重的射频干扰(RFI)问题,尤其在低频段。针对一种用于获取海洋盐度和土壤湿度的L波段相控阵微波辐射计,该文提出一种射频干扰检测算法。首先,简单介绍了该L波段相控阵微波辐射计系统;随后,详细介绍该射频干扰算法,其主要包括RFI初标识、RFI滑动窗口1次标识、RFI滑动窗口2次标识和RFI扩展标识等4个步骤;最后,采用该算法对L波段相控阵微波辐射计的实验数据进行处理。实验结果均表明:该算法能够较好地检测出射频干扰异常数据,检测性能较好。