The European Space Agency (ESA) launched the SMOS (Soil Moisture Ocean Salinity) mission in 2009; currently, multiple global soil moisture (SM) products are based on the measurements of its L-band (1.4 GHz) radiometer. We compared four SMOS products with each other: Level 2, Level 3, IC (INRA-CESBIO), and Near Real Time products. The comparisons focused on core validation sites (CVS), whose spatial representativeness errors allow the estimation of the SM product performance for bias-insensitive metrics (unbiased root mean square error (ubRMSE) and correlation (R), and anomaly R) with negligible uncertainty and for bias-sensitive metrics (mean difference (MD) and root mean square difference or RMSD) with acceptable uncertainty. When the products were compared with CVS independently, the results showed that the ubRMSE, R, and anomaly R of the IC product were better than those of the other products, while the MD was larger. However, the differences between the performances were smaller when the products were assessed using only the data points when each product had a valid retrieval. This indicates that the algorithms have similar performance and that data screening and quality flagging of the retrievals markedly affects the performance. The NASA Soil Moisture Active Passive (SMAP) mission produces a similar SM product as SMOS using an L-band radiometer. The closeness of the ubRMSE, R, and anomaly R performance of the IC product and the SMAP product (0.039 m3/m3vs. 0.041 m3/m3, 0.80 vs. 0.81, and 0.75 vs. 0.75) demonstrate that the SMOS and SMAP radiometers can achieve similar SM sensitivity.

The retrieval of soil moisture (SM) under forest canopy has long been an important goal for low frequency remote sensing. The NASA Soil Moisture Active Passive (SMAP) mission is engaged at three separate experiment sites to improve its SM retrieval algorithm in forested areas. Two of the sites are located in the deciduous forest region in Massachusetts and New York, US and one is located in southern boreal forest zone in Saskatchewan, Canada. Each site has a SM measurement network of 20-25 stations spread out over an area of about 30 km, which covers the SMAP radiometer footprint. In 2022, intensive observations will be carried out at each site which involve deployments of an airborne instrument, which is similar to the SMAP instrument, and intensive manual measurements of SM, surface and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. Here we show some early results using the networks and SMAP measurements to analyze the sensitivity of the SMAP L-band measurements to SM changes in forested area and the impact of the vegetation to the signal. The results suggest an upper limit for vegetation attenuation accounting for surface roughness effect and relate that to the values used in the current SMAP SM products.

Arctic environments are remote and inaccessible, making conventional field-based data collection challenging. Thus, this study describes an efficient desktop-based methodology for deriving reference data to support large-scale remote sensing classification focusing on wetland ecosystems. Our study area was Canada's Southern Arctic Ecozone. Various Earth observation (EO) datasets, including optical, multi-spectral, and topographic, were used as a base to support a photointerpretation process for collecting reference data. Ten 30-by-30-kilometer sampling plots were established across the ecozone for this activity based on a suite of minimum criteria. Reference polygons were assigned to one of the five major wetland classes of the Canadian Wetland Classification System (CWCS), along with a detailed wetland type definition. It is anticipated this methodology will be applied later to other northern ecozones to support large-scale wetland classification updates and status and trends reporting.

Ongoing evaluation of the soil moisture active passive (SMAP) soil moisture products has utilized validation networks distributed in several regions around the world. Thein situreference used for validation of the soil moisture retrieval algorithm is associated with measurements from soil moisture probes typically located at 5 cm beneath the soil surface; however, some networks also consider a vertically oriented probe that measures from 0 to 5 cm. In this study, we compare the correlation and unbiased root mean square error (ubRMSE) from the SMAP L2 radiometer soil moisture product when compared toin situmeasurements taken at 5 cm (approximately 3.5–6.5 cm) below the surface and measurements taken as an integrated measure from 0 to 5.7 cm. The data were obtained from two SMAP validation networks in Canada: the Kenaston network in Saskatchewan and Carman network situated in Manitoba. At both sites, correlations between thein situand the SMAP L2 product were consistently higher with vertically oriented probes following rain events. With respect to the ubRMSE, the vertically oriented probes at the Carman site had lower ubRMSE with the SMAP product than the horizontal probes that are currently used for validation activities. In some cases, vertical probe information should be considered in validation approaches when this data is available and could be considered in the design ofin situcalibration/validation networks. These results may be useful in design considerations of networks for upcoming soil moisture product validation.

Estimating soil moisture (SM) over the circumpolar boreal forest would have numerous applications including wildfire risk detection, and weather prediction. Evaluation of satellite derived SM retrievals in boreal ecoregions is hindered by available in situ SM observation networks. To address this, an SM monitoring network was established in a boreal forest region in Saskatchewan, Canada. The network is unique as there are no other SM network of similar size in the boreal forest. The network consisted of 17 SM stations within a single Soil Moisture Active Passive (SMAP) satellite observation pixel ($33\times 33$km). We present an analysis of the sensitivity and accuracy of SMAP SM products in a boreal forest environment over a two-year period in 2018 and 2019. Results show current SMAP radiometer-based L2 SM products have higher correlation with the in situ lower mineral layer SM than with the top organic layer, although the overall correlation is low. Correlations between in situ mineral layer SM and SMAP brightness-temperature (TB) products are higher than those observed with the SMAP SM product, suggesting current SMAP SM retrieval from the TB using the$\tau $–$\omega $model introduces large uncertainties in the SM estimation, possibly from uncertain vegetation and surface parameters in the retrieval model. Results show SM can be retrieved using the$\tau $–$\omega $model with reasonable accuracy over the boreal forest provided the vegetation and soil parameters are optimized. The SM retrieval using a dual channel$\tau $–$\omega $model, which utilize both horizontally and vertically polarized SMAP TB, performs better than that with a single channel algorithm (SCA), using optimized parameters.

The soil moisture active passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSD of not more than 0.04 m3/m3volumetric accuracy in the top 5 cm for vegetation with a water content of less than 5 kg/m2. Single-channel algorithm (SCA) and dual-channel algorithm (DCA) are implemented for the processing of SMAP radiometer data. The SCA using the vertically polarized brightness temperature (SCA-V) has been providing satisfactory SM retrievals. However, the DCA using prelaunch design and algorithm parameters for vertical and horizontal polarization data has a marginal performance. In this article, we show that with the updates of the roughness parameter h and the polarization mixing parameters Q, a modified DCA (MDCA) can achieve improved accuracy over DCA; it also allows for the retrieval of vegetation optical depth (VOD or τ). The retrieval performance of MDCA is assessed and compared with SCA-V and DCA using four years (April 1, 2015 to March 31, 2019) of in situ data from core validation sites (CVSs) and sparse networks. The assessment shows that SCA-V still outperforms all the implemented algorithms.

The NASA SMAP (Soil Moisture Active Passive) mission provides a global coverage of soil moisture measurements based on its L-band microwave radiometer every 2-3 days at about 40 km resolution. The soil moisture retrieval algorithms model the brightness temperature as a function of soil moisture, surface conditions and vegetation. External data sources inform the algorithms about the surface conditions and vegetation, which enable the retrieval of soil moisture. The inversion process contains uncertainties related to radiometer measurements, forward model assumptions and ancillary data sources. This study focuses on the uncertainties that depend on the seasonal evolution of the surface conditions and vegetation. The study compares the SMAP and core validation site (CVS) soil moisture values over a period of four years to extract the evolution of performance metrics over time. The analysis showed that most CVS that include managed agriculture exhibit significant time-dependent seasonal bias. This bias was linked to seasonal temperature cycle, which is a proxy to several features that can cause seasonally dependent errors in the SMAP product.

In order to calibrate and validate the SMAP soil moisture products, networks of ground-based soil moisture sensors have been deployed. Measurements collected from the networks must be upscaled to the radiometer footprint scale (30-40 km) for comparison with the SMAP radiometer-based retrievals. The upscaling is typically performed as a weighted average of individual sensor measurements within the SMAP grid. Since different weighting schemes have been found to result in different upscaled soil moisture estimates, an independent method of assessing soil moisture estimation biases is needed. We therefore present a method for calculating estimation biases at each SMAP Core Validation Site (CVS). The estimation was enabled by networks of enhanced soil moisture sampling that were deployed at four CVSs for a limited time. Based on Random Forests, our method offers a straightforward, systematic, and unified approach to bias estimation across a variety of sites. The method was applied to estimate biases at the four SMAP CVSs.

In the subarctic tundra, soil moisture information can benefit permafrost monitoring and ecological studies, but fine-scale remote-sensing approaches are lacking. We explore the suitability of C-band SAR, paying attention to two challenges soil moisture retrieval faces. First, the microtopography and the heterogeneous organic soils impart unique microwave scattering properties, even in absence of noteworthy shrub cover. Empirically, we find the polarimetric response is highly random (entropies >0.7). The randomness limits the applicability of purely polarimetric approaches to soil moisture estimation, as it causes a tailor-made decomposition to break down. For comparison, the L-band scattering response is more surfacelike, also in terms of its angular characteristics. The second challenge concerns the large spatial but small temporal variability of soil moisture observed at our site. Accordingly, the Radarsat-2 C-band backscatter has a limited dynamic range (~2 dB). However, contrary to polarimetric indicators, it shows a clear surface soil moisture signal. To account for the small dynamic range while retaining a 100-m spatial resolution, we embed an empirical time-series model in a Bayesian framework. This framework adaptively pools information from neighboring grid cells, thus increasing the precision. The retrieved soil moisture index achieves correlations of 0.3-0.5 with in situ data at 5 cm depth and, upon calibration, root-mean-square errors of3m-3. As this approach is applicable to Sentinel-1 data, it can potentially provide frequent soil moisture estimates across large regions. In the long term, L-band data hold greater promise for operational retrievals.

In this study we evaluate a Random Forest (RF) model for characterizing the spatial variability of soil moisture based on model derived from in situ soil moisture samples, geophysical data and RADAR observations. The RF model is run with and without C-band SAR backscatter to understand the importance of the inclusion of SAR data for mapping of soil moisture at field scale. The inclusion of SAR data in the RF resulted in a modest improvement however the geophysical parameters (e.g. soil types and terrain properties) were of greater importance.

Soil moisture plays a significant role in disciplines such as hydrology, meteorology and agriculture, and passive microwave remote sensing has become a widely used technique for global soil moisture estimation over the past three decades. Several satellite missions carrying radiometers have been launched over the past years. Among them are Japan Aerospace Exploration Agency's (JAXA's) Advanced Microwave Scanning Radiometer-EOS (AMSR-E) launched on NASA's Aqua satellite, European Space Agency's (ESA's) Soil Moisture and Ocean Salinity (SMOS) mission, JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite, and NASA's Soil Moisture Active Passive (SMAP) mission. Based on the availability of these four missions, there is an opportunity to develop a consistent inter-calibrated longterm soil moisture data record. This study focuses on the parametrization of the tau-omega model for soil moisture retrieval at L-, C- and X-band using brightness temperature observations from the four missions and in-situ soil moisture and soil temperature data from the SMAP core validation sites across various land cover types. The ancillary data sets used in the SMAP baseline algorithm are used for the retrievals at different frequencies. Simultaneous calibrations of the vegetation parameter b and roughness parameter h at both horizontal and vertical polarizations are performed. A set of model parameters to successfully retrieve soil moisture at different validation sites at L-, C- and X -band are presented. A preliminary comparison of SMAP and AMSR2 soil moisture retrievals against in situ observations at the Yanco (Australia) and TxSON (U.S.) sites showed the best accuracy and correlation at L-band (RMSD=0.03-0.05 m3/m3; R=0.92-0.94). The C-/X-band performance was not as satisfactory as L-band (RMSD=0.08-0.17 m3/m3; R=0.24-0.79). This also indicates that retrieval at higher frequencies can be very challenging when dense vegetation is present.

Permanently frozen and seasonally frozen soils occur over a large portion of the Earth's land surface. Changes in the freeze/thaw state of the land surface reflects major changes in thermal and hydraulic properties as well as acting as a “switch” for many ecological processes. In short, soil freeze/thaw state is a fundamental land surface variable in the water and energy cycles, and it connects to the carbon cycle. Surface freeze/thaw state is observable by passive and active microwave sensors. For example, NASA's Soil Moisture Active Passive (SMAP) mission includes a freeze/thaw data product. Such satellite sensing offers routine all-season and all-weather global observations of soil freeze/thaw state with the application of suitable algorithms. We describe early finding from the SLAPex Freeze/Thaw campaign, believed to be the first airborne campaign of its type, focusing on soil freeze/thaw.

Two L-Band radiometers were deployed at a boreal forest site (one above the forest canopy and one below the overstory canopy), to characterize the microwave contributions of the ground and forest canopy on the FreezelThaw (F/T) microwave signal observed by spaceborne L-Band sensors. Preliminary results show that the F/T processes of the ground rather than the overstory canopy primarily contribute to the F/T signal at L-band. However, the radiometer above the canopy did show some sensitivity to F/T of the canopy (5 K), but this sensitivity was much lower compared to the signal induced by the ground F/T (12 K).

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W) mission. AMSR2 fills the void left by the loss of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) after almost 10 years. Both missions provide brightness temperature observations that are used to retrieve soil moisture. Merging AMSR-E and AMSR2 will help build a consistent long-term dataset. Before tackling the integration of AMSR-E and AMSR2 it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites. Three products that rely on different algorithms were evaluated; the JAXA Soil Moisture Algorithm (JAXA), the Land Parameter Retrieval Model (LPRM), and the Single Channel Algorithm (SCA). Results indicate that overall the SCA has the best performance based upon the metrics considered.

Since the beginning of its routine science operation in March 2015, the NASA SMAP observatory has been returning interference-mitigated brightness temperature observations at L-band (1.41 GHz) frequency from space. The resulting data enable frequent global mapping of soil moisture with a retrieval uncertainty below 0.040 m3/m3at a 36 km spatial scale. This paper describes the development and validation of an enhanced version of the current standard soil moisture product. Compared with the standard product that is posted on a 36 km grid, the new enhanced product is posted on a 9 km grid. Derived from the same time-ordered brightness temperature observations that feed the current standard passive soil moisture product, the enhanced passive soil moisture product leverages on the Backus-Gilbert optimal interpolation technique that more fully utilizes the additional information from the original radiometer observations to achieve global mapping of soil moisture with enhanced clarity. The resulting enhanced soil moisture product was assessed using long-term in situ soil moisture observations from core validation sites located in diverse biomes and was found to exhibit an average retrieval uncertainty below 0.040 m3/m3. As of December 2016, the enhanced soil moisture product has been made available to the public from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center.

NASA's SMAP (Soil Moisture Active Passive) calibration and validation program revealed that the soil moisture products are experiencing difficulties in meeting the mission requirements in certain agricultural areas. Therefore, the mission organized airborne field experiments at two core validation sites to investigate these anomalies. The SMAP Validation Experiment 2016 included airborne observations with the PALS (Passive Active L-band Sensor) instrument and intensive ground sampling. The goal of the PALS measurements are to investigate the soil moisture retrieval algorithm formulation and parameterization under the varying (spatially and temporally) conditions of the agricultural domains and to obtain high resolution soil moisture maps within the SMAP pixels. In this paper the soil moisture retrieval using the PALS brightness temperature measurement in SMAPVEX16 is discussed in relation to in situ and SMAP soil moisture.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived standard soil moisture product (L2SMP) provides soil moisture estimates posted on a 36-km fixed Earth grid using brightness temperature observations and ancillary data. A beta quality version of L2SMP was released to the public in October, 2015, Version 3 validated L2SMP soil moisture data were released in May, 2016, and Version 4 L2SMP data were released in December, 2016. Version 4 data are processed using the same soil moisture retrieval algorithms as previous versions, but now include retrieved soil moisture from both the 6 am descending orbits and the 6 pm ascending orbits. Validation of 19 months of the standard L2SMP product was done for both AM and PM retrievals using in situ measurements from global core cal/val sites. Accuracy of the soil moisture retrievals averaged over the core sites showed that SMAP accuracy requirements are being met.

This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and −0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3ubRMSE, −0.015 m3/m3bias, and a correlation of 0.50, compared toin situmeasurements, thus meeting the accuracy target of 0.06 m3/m3ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation.

Many previous studies have shown the sensitivity of radar backscatter to surface soil moisture content, particularly at L-band. Moreover, the estimation of soil moisture from radar for bare soil surfaces is well-documented, but estimation underneath a vegetation canopy remains unsolved. Vegetation significantly increases the complexity of modeling the electromagnetic scattering in the observed scene, and can even obstruct the contributions from the underlying soil surface. Existing approaches to estimating soil moisture under vegetation using radar typically rely on a forward model to describe the backscattered signal and often require that the vegetation characteristics of the observed scene be provided by an ancillary data source. However, such information may not be reliable or available during the radar overpass of the observed scene (e.g., due to cloud coverage if derived from an optical sensor). Thus, the approach described herein is an extension of a change-detection method for soil moisture estimation, which does not require ancillary vegetation information, nor does it make use of a complicated forward scattering model. Novel modifications to the original algorithm include extension to multiple polarizations and a new technique for bounding the radar-derived soil moisture product using radiometer-based soil moisture estimates. Soil moisture estimates are generated using data from the Soil Moisture Active/Passive (SMAP) satellite-borne radar and radiometer data, and are compared with up-scaled data from a selection ofin situnetworks used in SMAP validation activities. These results show that the new algorithm can consistently achieve rms errors less than 0.07 m3/m3over a variety land cover types.

GCOM-W AMSR2 provides continuity following AMSR-E and the opportunity to generate a global long-term satellite soil moisture data record from the same instrument type. Various soil moisture products are being developed using AMSR observations. The JAXA soil moisture along with the Single Channel Algorithm (SCA) product were evaluated using in situ observations from different geographical domains. Both the JAXA and SCA soil moisture estimates capture the overall climatological features and the overall spatial structure of the two products is similar. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture. The SCA performs well over low and moderately vegetated areas. This study focuses on the development of the AMSR2 soil moisture product. Validation results using in situ observations from diverse climate and land cover conditions will be presented.

Surface soil moisture was retrieved globally by systematically correcting for the effects of vegetation and soil surface roughness. The retrieval is enabled by employing physical-models of radar forward scattering for individual vegetation types to account for vegetation scattering and absorption, and by constraining the surface roughness effect using time-series observations. The L-band SMAP multi-polarized (HH/VV/HV) σ° data acquired globally every three days were used from mid-April to early July, 2015. Assessment was conducted over 13 rigorously-chosen core validation sites covering a wide range of biomass types, biomass amount, and soil conditions. The soil moisture retrieval reached an accuracy of 0.06 m3/m3RMSE, a bias of 0.003 m3/m3, and a correlation of 0.56. The successful retrieval demonstrates that the physically-based retrieval method is capable of characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation on a global scale.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days using an L-band (active) radar and an L-band (passive) radiometer. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36 km fixed Earth grid using brightness temperature observations from descending (6 am) passes and ancillary data. A beta quality version of L2_SM_P was released to the public in September, 2015, with the fully validated L2_SM_P soil moisture data expected to be released in May, 2016. Additional improvements (including optimization of retrieval algorithm parameters and upscaling approaches) and methodology expansions (including increasing the number of core sites, model-based intercomparisons, and results from several intensive field campaigns) are anticipated in moving from accuracy assessment of the beta quality data to an evaluation of the fully validated L2_SM_P data product.

The database helps to better understand and quantify the effect of F/T and snow on the L-Band signal. The information will be useful for the validation and calibration of satellite based products. The database will also be used to validate and calibrate different L-Band snow emission models [3–4–5].

In order to provide a reliable source of ground-based validation data for the SMAP mission at spatial scales of 3 km, 9 km and 36 km, we have developed a new regression-based method capable of yielding highly-accurate upscaled soil moisture estimates based on sparse, irregularly-spaced soil moisture measurements.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development.

Passive microwave derived satellite soil moisture data was evaluated over in situ monitoring sites in Canada from two L-Band sensors. Soil moisture data from the Soil Moisture and Ocean Salinity (SMOS) and the Aquarius mission were used, which collect data at different spatial resolutions and using different retrieval models. Both sensors tend to underestimate soil moisture, with the underestimation from SMOS much more pronounced. Correlation coefficients show a reasonably good correspondence with in situ data, and this correlation tends to be better at sites where sub-grid soil moisture variability is represented in the in situ measured data. This highlights the importance of distributed in situ networks.

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.

This study was conducted as part of the Soil Moisture and Ocean Salinity (SMOS) calibration and validation activities over agricultural and boreal forest sites located in Saskatchewan, Canada. For each site covering 33 km × 71 km (i.e., about two SMOS pixels), we examined the SMOS brightness temperature (L1c) and soil moisture (L2) products from May 1 to September 30, 2010. The consistency of these data with respect to theory and to the temporal variation of surface characteristics was first discussed at both sites. Then, the SMOS L1c (prototype 346) and L2 (prototypes 305-309) products were evaluated using the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) ground measurements and L-band passive microwave airborne measurements, in addition to AMSR-E soil moisture estimates and simulations from the zeroth order τ- ω radiative transfer model. For both study sites, the model underestimated SMOS brightness temperatures in V polarization, whereas an overestimation was observed in H polarization. The data sets showed that both the SMOS and AMSR-E soil moisture values were underestimated compared with ground measurements collected during CanEx-SM10 but less so for the AMSR-E estimates. The SMOS soil moisture product was underestimated with a RMSE varying from 0.15 to 0.18 m3/ m3. Furthermore, the overall results showed that errors in the soil moisture estimates increased with the absolute value of soil moisture.

Our objective is to describe and evaluate the spatial variability of the surface soil moisture at small scales using Radarsat-2 images acquired over an agricultural area. To reach this aim, a geostatistical analysis is applied to up scale the soil moisture from ground measurements to the resolution of radar images and also from retrieving soil moisture at different spatial resolution. To conduct this study, four RADARSAT-2 images acquired at different modes during the summer of 2008 over agriculture fields located in Saskatoon (Saskatchewan, Canada) were used. The available data are ground measurements of soil moisture, surface roughness and vegetation characteristics. The comparison between retrieval and ground soil moisture showed average absolute relative errors of about 35% and 55% for fine and ScanSAR mode images respectively. Indeed, a weak amelioration was obtained from the up scaling of the ground soil moisture data to the spatial scale of the different radar image.

The target decompositions technique of Freeman-Durden is used to monitor freeze/thaw cycles, with fully polarimetric RADARSAT-2 images acquired over agricultural areas. In this paper is presented a preliminary analysis of the scattering mechanisms derived from three RADARSAT-2 acquisitions during the fall of 2008, over agricultural sites located in Saskatchewan, Ontario and Quebec. AMSR-E brightness temperatures data is also used in the analysis. Contrary to expectations, results indicate that surface scattering represents the main contribution to the signal over frozen soils.