[Show abstract][Hide abstract] ABSTRACT: Satellite-based passive microwave remote sensing has been shown to be a valuable tool in mapping and monitoring global soil moisture. The Advanced Microwave Scanning Radiometer on the Aqua platform (AMSR-E) has made significant contributions to this application. As the result of agency and individual initiatives, several approaches for the retrieval of soil moisture from AMSR-E have been proposed and implemented. Although the majority of these are based on the same Radiative Transfer Equation, studies have shown that the resulting soil moisture estimates can differ significantly. A primary goal of this investigation is to understand these differences and develop a suitable approach to potentially improve the algorithm currently used by NASA in producing its operational soil moisture product. In order to achieve this goal, the theoretical basis of several alternative soil moisture retrieval algorithms are examined. Analysis has focused on five established approaches: the operational algorithm adopted by NASA, which is referred to as the Normalized Polarization Difference algorithm, the Single Channel Algorithm, the Land Parameter Retrieval Model, the University of Montana soil moisture algorithm, and the HydroAlgo Artificial Neural Network algorithm. Previous comparisons of these algorithms in the literature have typically focused on the retrieved soil moisture products, and employed different metrics and data sets, and have resulted in differing conclusions. In this investigation we attempt to provide a more thorough understanding of the fundamental differences between the algorithms and how these differences affect their performance in terms of range of soil moisture provided. The comparative overview presented in the paper is based on the operating versions of the source codes of the individual algorithms. Analysis has indicated that the differences between algorithms lie in the specific parameterizations and assumptions of each algorithm. The comparative overview of the theoretical basis of the approaches is linked to differences found in the soil moisture retrievals, leading to suggestions for improvements and increased reliability in these algorithms.
[Show abstract][Hide abstract] ABSTRACT: The Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in the fall of 2014. This chapter reviews some of the best practices as related to soil moisture validation using in situ network observations that have been incorporated. There are four primary reasons why calibration and validation are necessary for a successful satellite mission: mission requirements, quality assurance, data integration, and science. The chapter provides an overview of some of the issues that were addressed in the development of the SMAP Calibration/Validation (Cal/Val) Plan. It discusses some sources of available guidance on the design of a validation program. The chapter then considers how this translates to soil moisture. In situ observations play a major role in the validation of satellite-based soil moisture and several aspects of using these data resources are discussed. Finally, the chapter describes the implementation of these ideas into the SMAP Cal/Val plan.
[Show abstract][Hide abstract] ABSTRACT: Overlapping soil moisture time series derived from two satellite microwave radiometers (the Soil Moisture and Ocean Salinity (SMOS) and the Advanced Microwave Scanning Radiometer-Earth Observing System) are used to generate a soil moisture time series from 2003 to 2010. Two statistical methodologies for generating long homogeneous time series of soil moisture are considered. Generated soil moisture time series using only morning satellite overpasses are compared to ground measurements from four watersheds in the U.S. with different climatologies. The two methods, cumulative density function (CDF) matching and copulas, are based on the same statistical theory, but the first makes the assumption that the two data sets are ordered the same way, which is not needed by the second. Both methods are calibrated in 2010, and the calibrated parameters are applied to the soil moisture data from 2003 to 2009. Results from these two methods compare well with ground measurements. However, CDF matching improves the correlation, whereas copulas improve the root-mean-square error.
IEEE Transactions on Geoscience and Remote Sensing 01/2014; 52(1):393-405. DOI:10.1109/TGRS.2013.2240691 · 3.51 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We coupled a radiative transfer model and a soil hydrological model (HYDRUS 1D) (Šimůnek et al., 2008) with an optimization routine to derive soil hydraulic parameters, surface roughness, and soil moisture of a tilled bare soil plot using measured brightness temperatures at 1.4 GHz (L-band), rainfall, and potential soil evaporation. The robustness of the approach was evaluated using five one-month data sets representing different meteorological conditions. We considered two soil hydraulic property models: the uni-modal Mualem van Genuchten and the bi-modal of Durner. Microwave radiative transfer was modeled by three different approaches: the Fresnel equation with depth averaged dielectric permittivity of either 2 or 5 cm thick surface layers and a coherent radiative transfer model (CRTM) that accounts for vertical gradients in dielectric permittivity. Brightness temperatures simulated by the CRTM and the 2-cm layer Fresnel model fitted well to the measured ones. L-band brightness temperatures are therefore related to the dielectric permittivity and soil moisture in a 2 cm thick surface layer. The surface roughness parameter that was derived from brightness temperatures using inverse modeling was similar to direct estimates from laser profiler measurements. The lab derived water retention curve was bi-modal and could be retrieved consistently for the different periods from brightness temperatures using inverse modeling. A uni-modal soil hydraulic property function underestimated the hydraulic conductivity near saturation. Surface soil moisture contents simulated using retrieved soil hydraulic parameters were compared with in-situ measurements. Depth specific calibration relations were essential to derive soil moisture from near-to-surface installed sensors.
Vadose Zone Journal 12/2013; DOI:10.2136/vzj2013.04.0075 · 1.78 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We present the most recent results from on-going collaborative sensor
inter-calibration and salinity inter-comparison analyses between SMOS
and Aquarius/SAC-D satellite ocean brightness temperature and salinity
retrievals. An important goal of both programs is to inter-calibrate
and combine the respective satellite data sets and in situ ocean
measurements to provide an accurate and well resolved ocean surface
salinity observing capability for ocean and climate studies. At the
time of this symposium, SMOS will have completed more that three years
of observations and Aquarius more than 19 months. This study will apply
the Aquarius data V2.0 to be released at the end of January 2013 (after
the submission of this abstract), which includes updated calibration,
pointing and geophysical model corrections. The comparison analyses
will consist of these elements: (1) Polarized brightness temperatures
(TH and TV) interpolated or synthesized at the Aquarius viewing angles,
(2) Level 2 salinity retrievals, (3) Level 3 gridded data, and (4) in
situ salinity data. The focus will be on quantifying the
inter-calibration biases between the sensors, relative uncertainties of
the salinity retrievals and the large-scale spatial and temporal
systematic biases between the two satellites and in situ data.
[Show abstract][Hide abstract] ABSTRACT: Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expe- ted that the SMOS estimates will improve.
IEEE Transactions on Geoscience and Remote Sensing 05/2012; 50(5):1530-1543. DOI:10.1109/TGRS.2011.2168533 · 3.51 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS) satellite were evaluated using in situ observations. The sites are located in different regions of the U.S. and provide replicate sampling of surface soil moisture at the SMOS footprint scale. Data from a sparse network were also considered. Soil moisture products from the Advanced Microwave Scanning Radiometer were also used for validation. Results based upon a preliminary version of the retrieval algorithm indicate promising performance. It is anticipated that the accuracy and reliability of the retrievals will improve as validation information is evaluated.
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International; 08/2011
[Show abstract][Hide abstract] ABSTRACT: The availability of global L-band observations from passive (the recently launched SMOS), and active (such as the PALSAR) microwave sensors has boosted the interest in making joint use of the two techniques to improve the retrieval of global near-surface soil moisture at unprecedented resolutions. The Soil Moisture Active Passive (SMAP) mission (scheduled launch, 2014) will fully exploit this synergy by providing concurrent active (radar) and passive (radiometer) microwave observations, resulting in passive-only, active-only and a merged active-passive soil moisture products at spatial resolutions of respectively 40km, 3km and 9km. The Soil Moisture Active Passive Experiments (SMAPEx) are a series of airborne field experiments specifically designed for algorithm development for SMAP and currently ongoing in the context of the SMAP pre-launch cal/val activities for Australia. Four SMAPEx campaigns are scheduled across the 2010-2011 seasonal cycle, with the first campaign (SMAPEx-1) successfully conducted on moderately wet winter conditions (July 5-10, 2010) and the second campaign (SMAPEx-2), scheduled for the summer (December 4-8,2010). SMAPEx is making use of a novel SMAP airborne simulator, including an L-band radar and radiometer to collect SMAP-like data over a well monitored semi-arid agricultural area in the Murrumbidgee catchment in south-eastern Australia. High resolution radar and radiometer observations collected during SMAPEx are supported by extensive ground sampling of soil moisture and ancillary data, allowing for testing of a variety of algorithms over semi-arid agricultural areas, typical of the Australian environment but similar to large areas of the central continental USA, including radiometer-only, radar-only, merged active-passive, downscaling and radar change-detection algorithms. In this paper a preliminary assessment of the performance of the radar-only and radiometer-only retrieval algorithms proposed as baseline for SMAP is presented. The soil moisture retrieved from active and passive microwave airborne observations collected during the SMAPEx-1 campaign is compared with extensive spatial data collected at focus areas. The quality of the individual retrievals is discussed in relation with different land surface conditions, ranging from intensive cropping to dryland grassland areas.
[Show abstract][Hide abstract] ABSTRACT: This paper discusses the results from a series of field experiments using ground-based L-band microwave active/passive sensors. Three independent approaches are applied to the microwave data to determine vegetation opacity of coniferous trees. First, a zero-order radiative transfer model is fitted to multi-angular microwave emissivity data in a least-square sense to provide “effective” vegetation optical depth. Second, a ratio between radar backscatter measurements with a corner reflector under trees and in an open area is calculated to obtain “measured” tree propagation characteristics. Finally, the “theoretical” propagation constant is determined by forward scattering theorem using detailed measurements of size/angle distributions and dielectric constants of the tree constituents (trunk, branches, and needles). The results indicate that “effective” values underestimate attenuation values compared to both “theoretical” and “measured” values.
Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International; 08/2010
[Show abstract][Hide abstract] ABSTRACT: Background/Question/Methods
Soil moisture drives ecological patterns and processes, yet cannot be accurately determined at large spatial scales from direct measurements. It has been one of NASA’s chief Earth science goals to overcome this barrier using satellite remote sensing, and leading NASA’s top tier missions next to be launched is the SMAP mission (Soil Moisture Active-Passive) aimed at measuring soil moisture from space. Using a combination of active radar and passive microwave sensing, SMAP is able to penetrate clouds and moderately thick canopies to detect soil moisture in the top 5 cm of soil. These data, which will be among the most accurate and broadly distributed remote sensing measurements of soil moisture available, are used in turn with land surface and other models to generate products of root zone soil moisture (9 km), freeze/thaw state (3 km), and net ecosystem exchange (1 km).
Vegetation cover can interfere significantly with remote sensing-based retrieval of soil moisture. This interference depends on vegetation structure as well as water content. We conducted a large-scale field experiment (CanEx) in Canada during June 2010 to support algorithm testing and development for the SMAP mission. A goal of this campaign was to assess performance of the SMAP soil moisture retrieval algorithms in a boreal landscape. During CanEX, airborne and satellite active and passive microwave (L-band) data were acquired as well as a large set of in situ vegetation and soil measurements.
The in situ data are used to parameterize microwave radiative transfer models to assess the influence of forest structure on the radar backscatter–in situ soil moisture measurement relationship. Coupling field measurements, remote sensing, and radiative transfer modeling improves our understanding of the vegetation effect on remote sensing retrievals of soil moisture while supporting algorithm development for the SMAP mission.
This work was conducted in part by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
[Show abstract][Hide abstract] ABSTRACT: In the near future two dedicated soil moisture satellites will be launched (SMOS and SMAP), both carrying an L-band radiometer. It is well known that microwave soil moisture retrieval algorithms must account for the physical temperature of the emitting surface. One proposed approach is the use of temperature output from numerical weather prediction (NWP) models. A radiative transfer model, as implemented in the most commonly used soil moisture retrieval algorithms, will be used to assess sensitivity to errors in the estimated surface temperature. It is shown that soil temperature errors will likely limit the vegetation range within which soil moisture can be retrieved with an accuracy of 0.04 m3m-3. These results should contribute to improved algorithm design and implementation for the new L-band satellite missions.
[Show abstract][Hide abstract] ABSTRACT: Microwave remote sensing can provide reliable measurements of surface soil moisture. However, there are a few land surface features that have a perturbing influence on the soil moisture retrievals. A lack of appropriate observations and physical characterization of target parameters contribute to retrieval problems. Also, some of these effects are relatively small and can be difficult to separate from other factors. Soil Moisture Experiments in 2005 (SMEX05) were designed to examine several aspects of soil moisture retrieval related to the WindSat satellite sensor. Early morning flights were conducted with an airborne microwave radiometer for several weeks from late June to early July 2005 in Iowa, USA over an agricultural domain (corn and soybean). Ground based measurements of soil moisture and related parameters were made concurrent with the aircraft and satellite observations. A focus on the early morning time frame provided an opportunity to study issues (specifically effect of dew on microwave emission) related to soil moisture retrieval during early morning hours, the observing time for WindSat and other future soil moisture satellites (SMOS, SMAP). Soil moisture estimates made using the aircraft X-band channel had a standard error of estimate of 0.053 m3/m3 for soybean and 0.064 m3/m3 for corn fields. Results of an experiment designed to observe the change in brightness temperature at X-band during the evaporation of dew in corn, soybean, and forest indicated that dew had a measurable impact. The presence of dew decreased land surface emissivity for each type of land cover. The impact of dew in corn was most significant and must be considered in soil moisture retrieval at X-band. Increases in temperature (and differences in canopy and soil temperature) during this period made it difficult to attribute all of the change in emissivity to dew dissipation.
[Show abstract][Hide abstract] ABSTRACT: The Soil Moisture Active Passive Mission (SMAP) is currently addressing
issues related to the development and selection of soil moisture
retrieval algorithms. A series of aircraft-based flights (SMAP
Validation Experiment 2008-SMAPVEX08) was designed to address some of
the issues that needed resolution. It was conducted on the Eastern Shore
of Maryland and Delaware over a two week period. The objectives of
SMAPVEX08 included: (1) development and evaluation of new radio
frequency interference (RFI) suppression techniques under consideration
for SMAP, (2) providing more robust sets of concurrent passive and
active L-band observational data, (3) evaluating the impact of azimuthal
orientation on alternative radar retrieval algorithms, and (4)
understanding the scaling of high resolution synthetic aperture radar
(SAR) to the lower resolution of SMAP. SMAPVEX08 was preceded by an
extended precipitation event that resulted in moist conditions. Cloud
cover and cooler fall temperatures resulted in a relatively slow but
consistent drydown of the surface soil. A series of seven aircraft
flights was conducted over two weeks that tracked this drydown. The key
instrument in SMAPVEX08 was the Passive Active L-band System (PALS),
which simulates SMAP. PALS observations provide a valuable
active-passive data set for the development of passive and active L-band
soil moisture estimates over heterogeneous land surface conditions.
Extensive ground observations were made concurrent with airborne
observations. The resulting brightness temperature images are consistent
with observed land surface conditions. Over the forested areas L-band
brightness temperatures show little variability throughout the duration
of the experiment. The standard error of estimate for soil moisture over
the Choptank watershed using only the radiometer observations was 0.039
m3/m3. Soil moisture algorithms using PALS radar observations are
currently being developed. Soil moisture retrieval results using passive
and active PALS observations from the SMAPVEX08 experiment will be
[Show abstract][Hide abstract] ABSTRACT: In the near future two dedicated soil moisture satellites will be
launched (SMOS and SMAP), both carrying an L-band radiometer. It is well
known that microwave soil moisture retrieval algorithms must account for
the physical temperature of the emitting surface. Solutions to this
include: difference, or ratio indices; forecast model products; thermal
infrared satellite observations; and high frequency passive microwave
estimates. The availability of multifrequency observations in the same
data stream has made the use of high frequency temperature estimates,
specifically 37 GHz (Ka-band), an attractive option. SMOS and SMAP will
not include a 37 GHz (Ka-band) microwave radiometer. Therefore,
alternative algorithms and data sources will be utilized and explored.
One proposed approach is the use of temperature output from numerical
weather prediction (NWP) models. This temperature estimate will need to
closely match the spatial resolution and the overpass time of SMOS and
SMAP (between 6 and 7 am/pm local time). To date, very little analysis
has been performed to assess the accuracy of the NWP forecasts in terms
of land surface temperature. In addition, the relationship between the
model products and the requirements of radiative transfer and soil
moisture retrieval algorithm temperature requirements needs to be
assessed. The goal of this paper is to set up a validation framework
that can be applied to NWP outputs. In this investigation, we use in
situ data from the Oklahoma Mesonet (at 5 cm) to assess the near surface
soil temperature from the Modern Era Retrospective-analysis for Research
and Applications (MERRA).
[Show abstract][Hide abstract] ABSTRACT: The spatial and temporal invariance of Soil Moisture and Ocean Salinity (SMOS) forward model parameters for soil moisture retrieval was assessed at 1-km resolution on a diurnal basis with data from the National Airborne Field Experiment 2006. The approach used was to apply the SMOS default parameters uniformly over 27 1-km validation pixels, retrieve soil moisture from the airborne observations, and then to interpret the differences between airborne and ground estimates in terms of land use, parameter variability, and sensing depth. For pastures (17 pixels) and nonirrigated crops (5 pixels), the root mean square error (rmse) was 0.03 volumetric (vol./vol.) soil moisture with a bias of 0.004 vol./vol. For pixels dominated by irrigated crops (5 pixels), the rmse was 0.10 vol./vol., and the bias was -0.09 vol./vol. The correlation coefficient between bias in irrigated areas and the 1-km field soil moisture variability was found to be 0.73, which suggests either 1) an increase of the soil dielectric roughness (up to about one) associated with small-scale heterogeneity of soil moisture or/and 2) a difference in sensing depth between an L-band radiometer and the in situ measurements, combined with a strong vertical gradient of soil moisture in the top 6 cm of the soil.