Soil Moisture Retrieval Algorithm Research Articles

Evaluation of Soil Moisture Retrieval from the ERS and Metop Scatterometers in the Lower Mekong Basin

The natural environment and livelihoods in the Lower Mekong Basin (LMB) are significantly affected by the annual hydrological cycle. Monitoring of soil moisture as a key variable in the hydrological cycle is of great interest in a number of Hydrological and agricultural applications. In this study we evaluated the quality and spatiotemporal variability of the soil moisture product retrieved from C-band scatterometers data across the LMB sub-catchments. The soil moisture retrieval algorithm showed reasonable performance in most areas of the LMB with the exception of a few sub-catchments in the eastern parts of Laos, where the land cover is characterized by dense vegetation. The best performance of the retrieval algorithm was obtained in agricultural regions. Comparison of the available in situ evaporation data in the LMB and the Basin Water Index (BWI), an indicator of the basin soil moisture condition, showed significant negative correlations up to R = −0.85. The inter-annual variation of the calculated BWI was also found corresponding to the reported extreme hydro-meteorological events in the Mekong region. The retrieved soil moisture data show high correlation (up to R = 0.92) with monthly anomalies of precipitation in non-irrigated regions. In general, the seasonal variability of soil moisture in the LMB was well captured by the retrieval method. The results of analysis also showed significant correlation between El Niño events and the monthly BWI anomaly measurements particularly for the month May with the maximum correlation of R = 0.88.

Evaluation of polarimetric Radarsat-2 SAR data for development of soil moisture retrieval algorithms over a chronosequence of black spruce boreal forests

C-band Radarsat-2 data were used to develop multi-parameter algorithms to retrieve soil moisture across a chronosequence (recently burned, shrubby regrowth and mature forest) of fire-disturbed Alaskan boreal black spruce forests using polarized backscatter intensity, polarimetric decomposition and discriminator parameters as independent variables. To account for complex interactions of surface roughness, biomass and soil moisture, multiple polarimetric variables were evaluated empirically in algorithm development using a time series dataset that included the wet and dry extreme conditions. Results indicate that polarimetric discriminators, such as maximum degree of polarization, fractional polarization or coefficient of variation, may be instrumental in accounting for the type of scattering occurring or the complexity or heterogeneity of the distributed target. Using the maximum degree of polarization combined with one or more variables on scattered intensity (e.g. C-HV, C-HH, or maximum scattering intensity) improved multi-linear moisture retrieval algorithms by 27–33% over the single, dual and quad polarized backscatter intensity algorithms. For all sites combined, algorithms were developed with low SE (7.0% volumetric soil moisture), moderately high R2 (0.77) and low RMSE (6.7% volumetric soil moisture). However, the best models were produced by separating the recently burned forests (strongest algorithm: R2 0.94, SE 5.9, RMSE 7.4) from the shrubby and mature forests (best algorithm: R2 0.85, SE 4.7, RMSE 7.3). Results are limited to sites with biomass below 3kg/m2.

Error Assessment of the Initial Near Real-Time METOP ASCAT Surface Soil Moisture Product

Since December 2008, the European Organisation for the Exploitation of Meteorological Satellites has been operationally distributing a global 25-km surface soil moisture product derived from the Advanced Scatterometer (ASCAT) onboard the meteorological operational platform (METOP) satellite METOP-A. Soil moisture is retrieved by using the semiempirical change detection method originally developed by the Vienna University of Technology (TU Wien) for the Active Microwave Instrument (AMI) flown on the European Remote Sensing (ERS) satellites ERS-1 and ERS-2. With the launch of the first of the three Meteorological Operational Platforms (METOP-A) in October 2006, ASCAT onboard METOP-A inherits and continues the role of his predecessor AMI. The original soil moisture retrieval algorithm (TU Wien model) was expected to be almost directly applicable for ASCAT with only minor changes, since the configuration and technical design is similar to the ERS scatterometers. Since the TU Wien model requires a robust historic long-term reference of scattering parameters, the initial near real-time METOP ASCAT soil moisture product had to rely on the model parameters derived from over 15 years of ERS-1/2. However, the combination of ASCAT backscatter measurements and ERS-1/2 historic long-term reference introduced some artifacts in the soil moisture product. The objectives of this paper were to analyze and investigate the impact of the ERS-1/2 historic long-term reference on the soil moisture retrieval. An error model has been developed to quantify the effects of the two main error sources: differences in spatial resolution and absolute calibration. The results of the study show that a simple model is able to describe the artifacts in the initial near real-time METOP ASCAT soil moisture product, which frequently occur in areas characterized by sharp backscatter contrasts. The expected overestimation of soil moisture using ERS-1/2 model parameters due to a calibration bias between AMI and ASCAT could be modeled as well.

Evaluating the L-MEB Model From Long-Term Microwave Measurements Over a Rough Field, SMOSREX 2006

The present paper analyzes the effects of roughness on the surface emission at L-band based on observations acquired during a long-term experiment. At the Surface Monitoring of the Soil Reservoir Experiment site near Toulouse, France, a bare soil was plowed and monitored over more than a year by means of an L-band radiometer, profile soil moisture and temperature sensors, and a local weather station, accompanied by 12 roughness campaigns. The aims of this paper are the following: 1) to present this unique database and 2) to use this data set to investigate the semiempirical parameters for the roughness in L-band Microwave Emission of the Biosphere, which is the forward model used in the Soil Moisture and Ocean Salinity soil moisture retrieval algorithm. In particular, we studied the link between these semiempirical parameters and the soil roughness characteristics expressed in terms of standard deviation of surface height (σ) and the correlation length (LC). The data set verifies that roughness effects decrease the sensitivity of surface emission to soil moisture, an effect which is most pronounced at high incidence angles and soil moisture and at horizontal polarization. Contradictory to previous studies, the semiempirical parameter Qr was not found to be equal to 0 for rough conditions. A linear relationship between the semiempirical parameters N and σ was established, while NH and NV appeared to be lower for a rough (NH ~ 0.59 and NV ~ -0.3) than for a quasi-smooth surface. This paper reveals the complexity of roughness effects and demonstrates the great value of a sound long-term data set of rough L-band surface emissions to improve our understanding on the matter.

The SMOS Soil Moisture Retrieval Algorithm

The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5°, flags and quality indices, and other parameters of interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises.

Soil moisture mapping over the central part of the Tibetan Plateau using a series of ASAR WS images

The wide swath (WS) and global monitoring (GM) modes of the Advanced Synthetic Aperture Radar (ASAR) provide for monitoring purposes a good compromise between the spatial coverage, spatial resolution and revisit time. Similar imaging modes will be a part of the Sentinel-1 SAR sensing system. A consequence of operating at the wider swath is, however, the rather large view angle range (about 16–43°), which needs to be corrected for if biophysical variables are to be retrieved from these data sets.A soil moisture retrieval algorithm based on the Integral Equation Method (IEM) scattering model is presented that explicitly simulates the backscatter as a function of the incidence angle instead of normalizing. This algorithm is applied to 150 ASAR WS scenes collected from April 2005 to September 2007 over the central part of the Tibetan Plateau. We selected three of these images covering the study area at different view angles during the winter season for the IEM roughness derivation. Subsequently, soil moisture is retrieved from the entire σo data sets via inversion of the IEM model with the obtained roughness parameters as input. The retrievals are validated against soil moisture measured from July 2006 to September 2007 at four sites representative for a 10×10km2 area. Further, the uncertainties following from assumptions made for the development of the algorithm are also quantified.

A Fusion Approach to Retrieve Soil Moisture With SAR and Optical Data

The retrieval of soil moisture in vegetated areas with active microwave remote sensing is a challenging process because scattering form the vegetated area incorporates the volume scattering from the vegetation cover and surface scattering from the underlying soil. In addition to this, vegetation provides two way attenuation for the signal scattering from the underlying soil. Therefore, retrieval of soil moisture requires such an approach that may adequately represent the scattering behavior from the vegetation covered area by defining the scattering term from the vegetation and vegetation covered soil clearly. Characterization of scattering due to vegetation is another cumbersome and complex process because it needs several vegetation parameters as input and important problem is that these vegetation parameters exhibit temporal behavior. Therefore, it is the need of present scenario to look for such an alternate approach that may not require the scattering characterization of the concerned vegetation moreover employs the ancillary information. Normalized difference vegetation index (NDVI) which can be obtained with optical data and is an indicator of vegetation, may be efficiently employed with SAR for retrieval of soil moisture in the vegetated area. With this aspect, present paper aims to fuse the information from PALSAR (Phased Array type L-band Synthetic Aperture Radar) and MODIS (Moderate Resolution Imaging Spectroradiometer) data to develop an algorithm for soil moisture retrieval in vegetation covered area. A knowledge based approach involving various polarizations (linear, circular, linear 45, co- and cross-polarized ratios for both linear and circular polarization) is used for land cover classification by which urban and water area can be masked. A normalized scattering based empirical model is developed where normalized coefficient is a function of vegetation characteristic (i.e., NDVI). The developed relationship provides the scattering coefficient of bare soil in HH- and VV-polarization and these values were subsequently used in Dubois Model, which has been solved with copolarization ratio approach to provide volumetric soil moisture content irrespective of roughness value. Two sets of images were used to test and validate the developed algorithm. The obtained results are in good agreement with the ground truth values and have potential to apply for soil moisture retrieval in large scale.

Effects of spatial distribution of soil parameters on soil moisture retrieval from passive microwave remote sensing

In this paper, we studied the effect of spatial distribution of soil parameters on passive soil moisture retrieval at pixel scale. First, we evaluated the forward microwave emission model and soil moisture retrieval algorithm accuracy through the observation of field experiments. Then, we used soil parameters in different spatial distribution patterns, including random, normal, and uniform distribution, to determine the different levels of heterogeneity on soil moisture retrieval, in order to seek the relationship between heterogeneity and soil moisture retrieval error. Finally, we conducted a controlled heterogeneity effect experiment measurements using a Truck-mounted Multi-frequency Radiometer (TMMR) to validate our simulation results. This work has proved that the soil moisture retrieval algorithm had a high accuracy (RMSE=0.049 cm3 cm−3) and can satisfy the need of this research. The simulation brightness temperatures match well with observations, with RMSE=9.89 K. At passive microwave remote sensing pixel scale, soil parameters with different spatial distribution patterns could have different levels of error on soil moisture estimation. Overall, we found that soil moisture with a random distribution in a satellite pixel scale can cause the largest error, with a normal distribution being the second, and a uniform distribution the least due to the smallest heterogeneity.

A proposed extension to the soil moisture and ocean salinity level 2 algorithm for mixed forest and moderate vegetation pixels

The Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009, provides global maps of soil moisture and ocean salinity by measuring the L-band (1.4 GHz) emission of the Earth's surface with a spatial resolution of 40–50 km. Uncertainty in the retrieval of soil moisture over large heterogeneous areas such as SMOS pixels is expected, due to the non-linearity of the relationship between soil moisture and the microwave emission. The current baseline soil moisture retrieval algorithm adopted by SMOS and implemented in the SMOS Level 2 (SMOS L2) processor partially accounts for the sub-pixel heterogeneity of the land surface, by modelling the individual contributions of different pixel fractions to the overall pixel emission. This retrieval approach is tested in this study using airborne L-band data over an area the size of a SMOS pixel characterised by a mix Eucalypt forest and moderate vegetation types (grassland and crops), with the objective of assessing its ability to correct for the soil moisture retrieval error induced by the land surface heterogeneity. A preliminary analysis using a traditional uniform pixel retrieval approach shows that the sub-pixel heterogeneity of land cover type causes significant errors in soil moisture retrieval (7.7%v/v RMSE, 2%v/v bias) in pixels characterised by a significant amount of forest (40–60%). Although the retrieval approach adopted by SMOS partially reduces this error, it is affected by errors beyond the SMOS target accuracy, presenting in particular a strong dry bias when a fraction of the pixel is occupied by forest (4.1%v/v RMSE, −3.1%v/v bias). An extension to the SMOS approach is proposed that accounts for the heterogeneity of vegetation optical depth within the SMOS pixel. The proposed approach is shown to significantly reduce the error in retrieved soil moisture (2.8%v/v RMSE, −0.3%v/v bias) in pixels characterised by a critical amount of forest (40–60%), at the limited cost of only a crude estimate of the optical depth of the forested area (better than 35% uncertainty). This study makes use of an unprecedented data set of airborne L-band observations and ground supporting data from the National Airborne Field Experiment 2005 (NAFE'05), which allowed accurate characterisation of the land surface heterogeneity over an area equivalent in size to a SMOS pixel.

A physically-based inversion algorithm for retrieving soil moisture in passive microwave remote sensing

► By reducing degrees of freedom of the Fresnel equation, a unique inversion algorithm (Hong’s algorithm) is developed for retrieving soil moisture in passive microwave remote sensing. ► The surface roughness and the dielectric constant are retrieved using Hong’s approximation. ► The Hong’s algorithm shows good consistency with field observations (SMEX03) and with current AMSR-E soil moisture algorithms. ► Consequently, the Hong’s algorithm is advantageous for an operational purpose with the minimal use of land surface forward models. Surface soil moisture plays a key role in water and energy exchanges at the land–atmosphere interface. Previous research on the microwave remote sensing of soil moisture focused mostly on the forward modeling problem. This paper presents a unique inversion algorithm (Hong’s algorithm) for soil moisture retrieval with reduced degrees of freedom of the Fresnel equation; the algorithm is based on the characteristics of the polarization ratio for rough surfaces around the Brewster angle and Hong’s approximation, which is an approximate relationship between vertically and horizontally polarized reflectivities. Using the daily observations of the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) taken at 0.25° × 0.25° pixel, the soil moisture is estimated globally over the land surface using a relation between the complex dielectric constant and the soil moisture after retrieving the surface roughness and complex dielectric constant. The surface soil moisture products obtained from Hong’s algorithm are validated using in situ observations carried out in Oklahoma and Georgia in 2003. The official AMSR-E soil moisture products are compared to assess Hong’s algorithm using identical brightness temperature data sets. The biases and the root mean squared error calculated in comparison with the ground observations are in the range of −0.059 m 3 /m 3 to 0.01 m 3 /m 3 and 0.023 m 3 /m 3 to 0.065 m 3 /m 3 , respectively. Hong’s algorithm estimates the soil moisture within the estimated accuracy of AMSR-E surface soil moisture, 0.06 m 3 /m 3 , without requiring a priori information about the roughness and the dielectric constants of the surface. With a minimal use of forward models, this research could also be applied to global soil moisture retrieval in the Soil Moisture and Ocean Salinity satellite mission and the Soil Moisture Active/Passive mission.

An Evaluation of Satellite Remote Sensing Data Products for Land Surface Hydrology: Atmospheric Infrared Sounder*

AbstractThe skill of instantaneous Atmospheric Infrared Sounder (AIRS) retrieved near-surface meteorology, including surface skin temperature (Ts), air temperature (Ta), specific humidity (q), and relative humidity (RH), as well as model-derived surface pressure (Psurf) and 10-m wind speed (w), is evaluated using collocated National Climatic Data Center (NCDC) in situ observations, offline data from the North American Land Data Assimilation System (NLDAS), and geostationary remote sensing (RS) data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Such data are needed for RS-based water cycle monitoring in areas without readily available in situ data. The study is conducted over the continental United States and Africa for a period of more than 6 years (2002–08). For both regions, it provides for the first time the geographic distribution of AIRS retrieval performance. Through conditional sampling, attribution of retrieval errors to scene atmospheric and surface conditions is performed. The findings support previous assertions that performance degrades with cloud fraction and that (positive) bias enhances with altitude. In general AIRS is biased warm and dry. In certain regions, strong AIRS–NCDC correlation suggests that bias-driven errors, which can be substantial, are correctable. The utility of the error characteristics for prescribing the input-induced uncertainty of RS retrieval models is demonstrated through two applications: a microwave soil moisture retrieval algorithm and the Penman–Monteith evapotranspiration model. An important side benefit of this study is the verification of NLDAS forcing.

The development of HJ SAR soil moisture retrieval algorithm

Four satellites with S-band synthetic aperture radar (SAR) onboard, along with four optical satellites, form the Huan Jing (HJ) constellation, which consists of the Chinese Environment and Disaster Monitoring Satellites that are designed for fast monitoring of the dynamics of soil moisture and other environmental variables. A 24-hour revisit time of the future four HJ SAR satellites enables an estimation of soil moisture change by using multi-temporal observations. The algorithm for estimating soil moisture change has been developed for HJ SAR in four steps: (a) theoretical simulation of radar backscattering from vegetated and bare soil with first-order scattering model and the Advanced Integral Equation Method (AIEM); (b) vegetation correction based on a simplified scattering model and ancillary optical observations of the HJ constellation; (c) simplification of a radar backscattering model for bare soil to separate the effects of surface roughness and soil moisture; and (d) estimation of soil moisture change from multi-temporal SAR observations. The comparison between field measurements and soil moisture retrieved by the proposed algorithm demonstrates good accuracy.

Sensitivity Analysis of b-factor in Microwave Emission Model for Soil Moisture Retrieval: A Case Study for SMAP Mission

Sensitivity analysis is critically needed to better understand the microwave emission model for soil moisture retrieval using passive microwave remote sensing data. The vegetation b-factor along with vegetation water content and surface characteristics has significant impact in model prediction. This study evaluates the sensitivity of the b-factor, which is function of vegetation type. The analysis is carried out using Passive and Active L and S-band airborne sensor (PALS) and measured field soil moisture from Southern Great Plains experiment (SGP99). The results show that the relative sensitivity of the b-factor is 86% in wet soil condition and 88% in high vegetated condition compared to the sensitivity of the soil moisture. Apparently, the b-factor is found to be more sensitive than the vegetation water content, surface roughness and surface temperature; therefore, the effect of the b-factor is fairly large to the microwave emission in certain conditions. Understanding the dependence of the b-factor on the soil and vegetation is important in studying the soil moisture retrieval algorithm, which can lead to potential improvements in model development for the Soil Moisture Active-Passive (SMAP) mission.

Analysis of a Least-Squares Soil Moisture Retrieval Algorithm from L-band Passive Observations

The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), launched on November 2009, is an unprecedented initiative to globally monitor surface soil moisture using a novel 2-D L-band interferometric radiometer concept. Airborne campaigns and ground-based field experiments have proven that radiometers operating at L-band are highly sensitive to soil moisture, due to the large contrast between the dielectric constant of soil minerals and water. Still, soil moisture inversion from passive microwave observations is complex, since the microwave emission from soils depends strongly on its moisture content but also on other surface characteristics such as soil type, soil roughness, surface temperature and vegetation cover, and their contributions must be carefully de-coupled in the retrieval process. In the present study, different soil moisture retrieval configurations are examined, depending on whether prior information is used in the inversion process or not. Retrievals are formulated in terms of vertical (Tvv) and horizontal (Thh) polarizations separately and using the first Stokes parameter (TI ), over six main surface conditions combining dry, moist and wet soils with bare and vegetation-covered surfaces. A sensitivity analysis illustrates the influence that the geophysical variables dominating the Earth’s emission at L-band have on the precision of the retrievals, for each configuration. It shows that, if adequate constraints on the ancillary data are added, the algorithm should converge to more accurate estimations. SMOS-like brightness temperatures are also generated by the SMOS End-to-end Performance Simulator (SEPS) to assess the retrieval errors produced by the different cost function configurations. Better soil moisture retrievals are obtained when the inversion is constrained with prior information, in line with the sensitivity study, and more robust estimates are obtained using TI than using Tvv and Thh. This paper analyzes key issues to devise an optimal soil moisture inversion algorithm for SMOS and results can be readily transferred to the upcoming SMOS data to produce the much needed global maps of the Earth’s surface soil moisture.

ELBARA II, an L-Band Radiometer System for Soil Moisture Research

L-band (1–2 GHz) microwave radiometry is a remote sensing technique that can be used to monitor soil moisture, and is deployed in the Soil Moisture and Ocean Salinity (SMOS) Mission of the European Space Agency (ESA). Performing ground-based radiometer campaigns before launch, during the commissioning phase and during the operative SMOS mission is important for validating the satellite data and for the further improvement of the radiative transfer models used in the soil-moisture retrieval algorithms. To address these needs, three identical L-band radiometer systems were ordered by ESA. They rely on the proven architecture of the ETH L-Band radiometer for soil moisture research (ELBARA) with major improvements in the microwave electronics, the internal calibration sources, the data acquisition, the user interface, and the mechanics. The purpose of this paper is to describe the design of the instruments and the main characteristics that are relevant for the user.

The SMOS Mediterranean Ecosystem L-Band characterisation EXperiment (MELBEX-I) over natural shrubs

In the framework of ESA's SMOS mission (Soil Moisture and Ocean Salinity), many studies have been carried out over different land surface types to model their microwave emission at L-band (1.4 GHz). Results of these studies have been integrated in the emission model L-MEB (L-Band Microwave Emission of the Biosphere), which is the core of the SMOS Level 2 soil moisture retrieval algorithm. The Mediterranean Ecosystem L-Band characterisation EXperiment (MELBEX-I) was carried out at the SMOS validation site near Valencia in autumn 2005. The main objective of MELBEX-I was to calibrate L-MEB over Mediterranean shrub land, as no data were available over this biome. For that purpose, multi-angular and dual polarimetric measurements ( H, V) were obtained by the EMIRAD L-band radiometer from a 14-m tower. Results of this study indicate a small and constant impact of vegetation on the microwave emission of shrub land, and L-MEB parameters for shrub land were obtained. In addition, the study highlights the need for calibrating microwave soil roughness, which was found to be constant at the site. Depending on the number of retrieved parameters, soil moisture (SM) near the surface could be estimated with errors between 0.035 m 3 m − 3 (if only SM was retrieved) and 0.057 m 3 m − 3 (if SM, optical depth and a roughness parameter were simultaneously retrieved). Finally, no modelling improvements were observed when coarse estimates of the fraction of exposed rocks were accounted for in the model.

Parameterization of the Land Parameter Retrieval Model for L-Band Observations Using the NAFE'05 Data Set

The Land Parameter Retrieval Model (LPRM) has been successfully applied to retrieve soil moisture from space-borne passive microwave observations at C-, X-, or Ku-band and high incidence angles (50deg-55 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deg</sup> ). However, LPRM had never been applied to lower angles or to L-band observations. This letter describes the parameterization and performance of LPRM using aircraft and ground data from the National Airborne Field Experiment 2005. This experiment was undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. It was found that model convergence could only be achieved with a temporally dynamic roughness. The roughness was parameterized according to incidence angle and soil moisture. These findings were integrated in LPRM, resulting in one uniform parameterization for all sites. The parameterized LPRM correlated well with field observations at 5-cm depth ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> = 0.93 based on all sites) with a negligible bias and an accuracy of 0.06 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> middotm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> . These results demonstrate comparable retrieval accuracies as the official SMOS soil-moisture retrieval algorithm (L-MEB), but without the need for the ancillary data that are required by L-MEB. However, care should be taken when using the proposed dynamic roughness model as it is based on a limited data set, and a more thorough evaluation is necessary to test the validity of this new approach to a wider range of conditions.

Development of a physically-based soil moisture retrieval algorithm for spaceborne passive microwave radiometers and its application to AMSR-E

Development of a physically-based soil moisture retrieval algorithm for spaceborne passive microwave radiometers and its application to AMSR-E

An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations

The scatterometers onboard the European Remote Sensing satellites (ERS-1 & ERS-2) and the METeorological OPerational satellite (METOP) have been shown to be useful for surface soil moisture retrieval using the so-called TU-Wien change detection method. This paper presents an improved soil moisture retrieval algorithm based on the existing TU-Wien method but with new parameterization as well as a series of modifications. The new algorithm, WAter Retrieval Package 5 (WARP5), copes with some limitations identified in the earlier method WARP4 and provides the possibility of migrating soil moisture retrieval from ERS-SCAT to METOP-ASCAT data. The WARP5 algorithm results in a more robust and spatially uniform soil moisture product, thanks to its new processing elements, including a method for the correction of azimuthal anisotropy of backscatter, a comprehensive noise model, and new techniques for calculation of the model parameters. Cross-comparisons of WARP4 and WARP5 data sets with the Oklahoma Mesonet in situ observations and also with European Centre of Medium Range Weather Forecast (ECMWF) ReAnalysis (ERA-Interim) global modeled data show that the new algorithm has a better performance and effectively corrects retrieval errors in certain areas.

Empirically Adopted IEM for Retrieval of Soil Moisture From Radar Backscattering Coefficients

The integral equation model (IEM) is considered as a promising algorithm for soil moisture retrieval from active microwave data over bare soil and sparsely vegetated conditions. However, the soil dielectric constant is implicitly embedded in the complicated IEM; inversion of soil moisture is often accomplished through iteration and is thus computationally expensive, particularly when it is applied to retrieve soil moisture from active microwave data on a large scale. To simplify the inversion process of soil moisture directly from the active microwave data, basic math functions were adopted to fit the simulation results of the original IEM so that the radar backscattering coefficient becomes an explicit function of soil dielectric constant or the soil dielectric constant is an explicit function of radar backscattering coefficient. Soil moisture is then calculated directly from radar backscattering coefficient without iteration. We called this model empirically adopted IEM (EA-IEM). The accuracy of the EA-IEM to the original IEM and its applicability are analyzed through three processes: model intercomparison, sensitivity analysis, and model comparison with <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">situ</i> measurements. The average differences of backscattering coefficients between the EA-IEM and the original IEM are 0.14 dB for HH-polarization and 0.12 dB (Gaussian correlation function) and 0.2 dB (exponential correlation function) for VV-polarization. The sensitivity of soil moisture variation is examined under the consideration of absolute and relative calibration errors. A comparison between the soil moisture estimated and the measurements is performed, and the root-mean-square (rms) error is found to be 3.4%, suggesting that the EA-IEM performs well in these real cases. All these analyses indicate that the EA-IEM is a good representative of the original IEM and can be used to retrieve soil moisture under the tested range of model parameters: incidence angles between 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deg</sup> and 60 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deg</sup> , soil dielectric constants between 4 and 42, surface rms height from 4 to 31 mm, and correlation length from 50 to 250 mm.