Understanding the vertical distribution of soil moisture is crucial for making informed decisions in various applications, ranging from precision agriculture to hydrological modeling. Four machine learning algorithms, including random forest, extreme gradient boosting, deep learning, and support vector regression were employed to estimate the soil moisture profile from collected tower-based L-band and P-band brightness temperature observations in Victoria, Australia. The results showed that random forest outperformed the other algorithms, with root mean square error (RMSE) values of 0.03, 0.04, and 0.06 m3/m3for depths of 0-5 cm, 0-30 cm, and 0-60 cm, respectively

As an emerging technique, P-band (0.3-1 GHz) may improve soil moisture remote sensing compared to L-band (1.4 GHz) SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, because of its greater moisture retrieval depth resulting from its longer wavelength. Consequently, a number of tower-based experiments were undertaken in Victoria, Australia, to understand and quantify potential improvements. The study reported here has extended the evaluation of the tau-omega model to a scenario with a dense corn canopy whose vegetation water content reached ~20 kg/m2, and compared the soil moisture retrieval performance at P- and L-band. Based on the locally calibrated parameters, the results from both the SCA (Single Channel Algorithm) and DCA (Dual Channel Algorithm) approaches presented a clear reduction in vegetation impact at P-band compared to L-band. While the root-mean-square error (RMSE) for P-band did not achieve the 0.04-m3/m3target accuracy of SMOS and SMAP, i.e., 0.054 m3/m3for the SCA and 0.074 m3/m3for the DCA, this performance can be regarded as acceptable considering the extremely high vegetation water content. In comparison, the RMSEs at L-band were larger than 0.1 m3/m3for both the SCA and the DCA approaches. Additionally, DCA performed better in correlation coefficient and unbiased RMSE, while SCA performed better in RMSE at P-band due to the larger bias when using DCA. Moreover, the calibrated vegetation parameters at P-band were found to apply to broader conditions than those at L-band, likely due to the reduced vegetation impact.

Root zone soil moisture and its distribution throughout the profile play an important role in agricultural productivity and drought monitoring. An inversion scheme including the stratified coherent models of Njoku and Wilheit were employed to retrieve the daily soil moisture profile at 6 AM from simulated L-band and P-band radiometry observations for April 2019 in Cora Lynn, Victoria, Australia. Different levels of noise up to 4 K were imposed in this synthetic study. The average RMSE of retrieved soil moisture at the surface, middle, and bottom (60 cm) of the profile for the Njoku (Wilheit) model were 0.01 (0.04), 0.04 (0.06), and 0.05 (0.07) (all in m3/m3) when a second-order polynomial function was considered as the representative of the soil moisture profile.

The moisture retrieval depth is commonly held to be the approximately top 5 cm at L-band (~21-cm wavelength/1.41 GHz), which is seen as a limitation for hydrological applications. A widely held view is that this moisture retrieval depth increases with wavelength, ranging approximately from one-tenth to one-fourth of the wavelength. Accordingly, P-band (~40-cm wavelength/0.75 GHz) is under investigation for soil moisture observation over a deeper layer of soil. However, there is no accepted method for predicting the moisture retrieval depth, and there has been no study to confirm that the actual retrieval depth at P-band is indeed deeper than that achieved at L-band. Consequently, this research has estimated the moisture retrieval depth from theory and compared with empirical evidence from tower-based observations. Model predictions and experimental observations agreed that P-band has the potential to retrieve soil moisture over a deeper layer (~7 cm) than L-band (~5 cm) while maintaining the same correlation. However, an alternate interpretation of experimental results is that P-band has a larger correlation with soil moisture (accuracy of retrieval) than L-band but for the same 5-cm moisture retrieval depth. The results also demonstrated the increasing trend of the moisture retrieval depth for increasing wavelength, with the potential to achieving a moisture retrieval depth greater than 10 cm for P-band below 0.5 GHz. Importantly, model predictions showed that moisture retrieval depth was not only dependent on soil moisture content and observation frequency, but also the moisture gradient of the profile.

The fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4 and -5) were conducted at the beginning of the SMAP operational phase, May and September 2015, to: 1) evaluate the SMAP microwave observations and derived soil moisture (SM) products and 2) intercompare with the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions over the Murrumbidgee River Catchment in the southeast of Australia. Airborne radar and radiometer observations at the same microwave frequencies as SMAP were collected over SMAP footprints/grids concurrent with its overpass. In addition, intensive ground sampling of SM, vegetation water content, and surface roughness was carried out, primarily for validation of airborne SM retrieval over six ~ 3 km × 3 km focus areas. In this study, the SMAPEx-4 and -5 data sets were used as independent reference for extensively evaluating the brightness temperature and SM products of SMAP, and intercompared with SMOS and Aquarius under a wide range of SM and vegetation conditions. Importantly, this is the only extensive airborne field campaign that collected data while the SMAP radar was still operational. The SMAP radar, radiometer, and derived SM showed a high agreement with the SMAPEx-4 and -5 data set, with a root-mean-squared error (RMSE) of ~3 K for radiometer brightness temperature, and an RMSE of ~ 0.05 m3 for the radiometer-only SM product. The SMAP radar backscatter had an RMSE of 3.4 dB, while the retrieved SM had an RMSE of 0.11 m3/m3 when compared with the SMAPEx-4 data set.

Soil Moisture is an important geophysical variable that needs reliable quantification for applications in hydrology, meteorology and agriculture. L-band radiometry has proved to be one of the best methods in soil moisture estimation using microwave signals. However, they provide measurements that correspond to a shallow depth of 5 cm and are also affected by the presence of overlaying vegetation and roughness. In contrast, P-band radiometry is expected to provide moisture information on a deeper layer of soil. Moreover, these lower frequency measurements are expected to be less affected by soil roughness and vegetation contributions. Consequently, this pilot study uses the Polarimetric P-band Multibeam Radiometer (PPMR) at 740 MHz to evaluate the response of the P-band radiometer over a realistic range of surface conditions at the field scale. A preliminary framework of P-band Microwave Emission of the Biosphere (P-MEB) has been developed as a forward model that simulates brightness temperature from soil moisture and other ancillary data collected from the field. This paper presents the model for the bare soil condition observed during June 2018 to August 2018. The results show that H-polarised PPMR data has better correlation to the soil moisture over a depth of 10 cm than the V-polarized PPMR data. A model is under improvement by incorporating a more suitable effective temperature formulation.

Readily available soil moisture data will help farmers to better optimize their irrigation scheduling and minimize water consumption. Consequently, there is a large and accelerating interest in using sensing technologies in precision agriculture worldwide. Among them, passive microwave sensing technology has been considered as the most accurate in retrieving soil moisture. This study compares the performance of an L-band radiometer system at two different platforms: airborne and near-surface (buggy). Field experiments have been conducted across an agricultural site in Tasmania, Australia for three consecutive days for. Ground sampling was also conducted in order to evaluate the accuracy of both L-band radiometer systems. Brightness temperature data from the buggy showed a larger temporal variation on individual days than the aircraft data, likely due to the irrigation activity during the longer period required for data collection by the buggy than for the aircraft. In terms of the relationship between brightness temperature and soil moisture, both platforms showed similar results while the buggy-based data showed a slightly better correlation with soil moisture.

Soil moisture measurement using L-band radiometry is now widely accepted as the state-of-art remote sensing approach, and has been adopted by both the SMOS and SMAP soil moisture dedicated satellite missions. However, it suffers from the shallow depth of its soil moisture measurement, and the confounding effects of vegetation and soil roughness on soil moisture retrieval. P-band, which is a longer wavelength measurement, provides the potential to retrieve deeper soil moisture information, and to do so more accurately due to reduced soil roughness and vegetation effects. This paper presents some pioneering work on the use of P-band for soil moisture retrieval. The Polarimetric P-band Multibeam Radiometer (PPMR) used in this research operates at 740 MHz / wavelength of 40 cm. It is used together with the Polarimetric L-band Multibeam Radiometer (PLMR) which operates at 1.4 GHz / wavelength of 21 cm. The PPMR and PLMR are mounted onto a 10m high tower in an agricultural farm located at Cora Lynn, Victoria. This paper outlines the initial set up for the study and the experimental plan for understanding PPMR's performance, along with some initial data.

Soil moisture content is an essential climate variable that is operationally delivered at low resolution (e.g. 36-9 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of soil moisture maps at higher resolution. For this reason, there is a large research effort to develop soil moisture products at higher resolution using, for instance, data acquired by the new ESA's Sentinel missions. The objective of this study is twofold. First, it presents the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution. Second, it assesses the possibility of integrating Sentinel-2 data and additional ancillary information, such as parcel borders and high resolution soil texture maps, in order to obtain soil moisture maps at “field scale” resolution, i.e. ~0.1 km. Case studies concerning agricultural sites located in Europe are presented.

A fundamental limitation of current soil moisture remote sensing technology is that can only provide moisture information on the top 5 cm layer of soil at most, being one-tenth to one-quarter of the wavelength (21 cm at L-band; 1.4 GHz) using the current SMAP and SMOS soil moisture dedicated missions of NASA and ESA. Consequently, we have developed an airborne passive microwave sensing capability at P-band to develop a new state-of-the-art satellite concept that will provide soil moisture data for the top 10 cm layer of soil using radiometer observations at P-band (40 cm; 750 MHz). Not only would P-band provide soil moisture information on a soil layer thickness that more closely relates to that affecting crop and pasture growth, but it is expected to produce greater spatial coverage with improved accuracy to that from L-band. This is because P-band should be less affected by surface roughness conditions and have a reduced attenuation by the overlaying vegetation. This paper describes a series of small airborne field experiments at P-band, and presents some early results of P-band passive microwave observations in comparison with L-band and K-band passive microwave from initial trial flights.

The aim of this paper was to test the capabilities of the Sentinel-1 radar data in downscaling Soil Moisture Active Passive (SMAP) radiometer data for high-resolution soil moisture estimation. Three different active-passive downscaling algorithms, including the brightness temperature-based downscaling algorithm (BTBDA), the soil moisture-based downscaling algorithm (SMBDA), and a change detection method (CDM), were analyzed using pairs of Sentinel-1 active and SMAP passive observations collected over a semiarid landscape in southeastern Australia from May 2015 to May 2016. While these algorithms have been tested previously, this is the first study to evaluate the three algorithms using real Sentinel-1 radar and SMAP radiometer data. The SMAP passive observations were disaggregated to 9-, 3-, and 1-km scales and then compared with ground soil moisture measurements. The results suggest that the root-mean-square error (RMSE) in downscaled soil moisture at 9-km resolution was 0.057, 0.056, and 0.067 cm3/cm3for the BTBDA, SMBDA, and CDM, respectively. The accuracy of downscaling methods was generally decreased when applied at the finer spatial resolution. The SMBDA had overall better performance in terms of correctly detecting the soil moisture pattern and relatively lower RMSE values, and is, therefore, recommended for the combined Sentinel-1 radar and SMAP radiometer setup for soil moisture monitoring. The influence of incidence angle normalization of Sentinel-1 SAR data on downscaled soil moisture was also investigated and found to be minimal.

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.

The systematic retrieval of near surface soil moisture (SSM) fields at high resolution (e.g., 0.1-1.0 km) is a challenging task that requires the exploitation of new retrieval algorithms and SAR data with advanced observational capabilities (in terms of spatial/temporal resolution, radiometric accuracy, very large swath, long-term continuity and rapid data dissemination). The launch of the Sentinel-1 (S-1) constellation provides these capabilities and calls for the development and validation of pre-operational SSM products at high resolution. The objective of this paper is to present and initially assess a SSM retrieval algorithm developed in view of S-1 data exploitation. The activity is supported by a large scientific community engaged in fostering a more effective interaction between researchers working in the field of high and low resolution SSM retrieval.

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.

Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and the atmosphere. Passive microwave remote sensing at L-band can capture spatial and temporal patterns of soil moisture in the landscape. Both ESA and NASA have launched L-band radiometers, in the form of the SMOS and SMAP satellites respectively, to monitor soil moisture globally, every 3-day at about 40 km resolution. However, their coarse scale restricts the range of applications. While SMAP included an L-band radar to downscale the radiometer soil moisture to 9 km, the radar failed after 3 months and this initial approach is not applicable to developing a consistent long term soil moisture product across the two missions anymore. Existing optical-, radiometer-, and oversampling-based downscaling methods could be an alternative to the radar-based approach for delivering such data. Nevertheless, retrieval of a consistent high resolution soil moisture product remains a challenge, and there has been no comprehensive intercomparison of the alternate approaches. This research undertakes an assessment of the different downscaling approaches using the SMAPEx-4 field campaign data.

Three active-passive soil moisture downscaling algorithms are tested to demonstrate the feasibility of each for application to NASA's Soil Moisture Active Passive (SMAP) mission launched in January 2015. These algorithms include the official baseline and optional downscaling algorithms, and a change detection method. These synergistically use 1-km synthetic aperture radar backscatter to downscale 36-km brightness temperature to 9 km, which is then converted into soil moisture at 9 km, or downscale soil moisture directly to 9-km resolution. While these algorithms have been tested previously, this was mostly using synthetic data sets. Moreover, there has never before been a direct comparison of the alternate methods using the same data sets. Thus, it is imperative that they be tested against each other for a comprehensive range of land surface conditions prior to global application. Consequently, this letter evaluates these three algorithms using data collected from the soil moisture active passive experiments (SMAPExs) in Australia, designed to closely simulate the SMAP data stream for a single SMAP radiometer pixel over a three-week interval. Results suggested that the average root-mean-square error (RMSE) in downscaled soil moisture at 9-km resolution was 0.019, 0.021, and 0.026 cm3/cm3for the baseline, optional, and change detection method, respectively. While there was a little difference in the RMSE, the optional method showed the best correlation between the downscaled soil moisture and the reference soil moisture map. Therefore, the optional algorithm is recommended for global implantation by SMAP.

The National Aeronautics and Space Administration's Soil Moisture Active Passive (SMAP) mission, launched in January 2015, was designed to provide a global soil moisture product at medium resolution (~9 km), by combining observations from its radar and radiometer. Several downscaling methods have been proposed by the SMAP team for this purpose. This paper evaluates another candidate downscaling method, namely, the Bayesian merging approach. While this has been tested using a synthetic data set across the USA, it is imperative that it can also be tested using the experimental data for a comprehensive range of land surface conditions (i.e., in different hydro-climatic regions) prior to a global application. Consequently, this paper applies this method using the data collected from SMAP experiments field campaigns in southeastern Australia that closely simulated the SMAP data stream for a single SMAP radiometer pixel over a three-week interval. The method studied here differs from the linear downscaling methods of the SMAP mission, in that it uses a nonlinear method based on Bayes' theorem. The medium-resolution soil moisture product is obtained using background soil moisture estimates that are updated according to the difference between the observed and predicted brightness temperatures and backscatter coefficients, relating the highand low-resolution data. Results were assessed against a reference soil moisture map derived from high-resolution airborne radiometer observations. The rootmean-square-error and R2for the Bayesian merging method were found to be 0.02 cm3/cm3and 0.55, respectively, at 9-km resolution, being similar to the SMAP's “optional” downscaling method.

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 L-band (1 - 2 GHz) microwave remote sensing has been widely acknowledged as the most promising method to monitor regional to global soil moisture. Consequently, the Soil Moisture Active Passive (SMAP) satellite applied this technique to provide global soil moisture every 2 to 3 days. To verify the performance of SMAP, the fourth and fifth campaign of SMAP Experiments (SMAPEx-4 & -5) were carried out at the beginning of the SMAP operational phase in the Murrumbidgee River catchment, southeast Australia. The airborne radar and radiometer observations together with ground sampling on soil moisture, vegetation water content, and surface roughness were collected in coincidence with SMAP overpasses. The SMAPEx-4 & -5 data sets will benefit to SMAP post-launch calibration and validation under Australian land surface conditions.

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.

Information on vegetation water content (VWC) is important in retrieving soil moisture using microwave remote sensing. It can be also used for other applications, including drought detection, bushfire prediction, and agricultural productivity assessment. Through the Soil Moisture Active Passive (SMAP) mission of the National Aeronautics and Space Administration, radar data may potentially provide the VWC information needed for soil moisture retrieval from the radiometer data acquired by the same satellite. In this paper, VWC estimation is tested using radar vegetation index (RVI) data from the third SMAP airborne Experiment. Comparing with coincident ground measurements, prediction equations for wheat and pasture were developed. While a good relationship was found for wheat, with r = 0.49, 0.62, and 0.65 and root-mean-square error (RMSE) = 0.42, 0.37, and 0.36 kg/m2, the relationship for pasture was poor, with r = -0.06, -0.14, and - 0.002 and RMSE = 0.15, 0.15, and 0.15, kg/m2, for 10-, 30-, and 90-m resolutions, respectively. These results suggested that RVI is better correlated with VWC for vegetation types having a greater dynamic range. However, the results were not as good as those from a previous tower-based study (r = 0.98 and RMSE = 0.12 kg/m2) over wheat. This is possibly due to spatial variation in vegetation structure and surface roughness not present in tower studies. Consequently, results from this study are expected to more closely represent those from satellite observations such as SMAP, where large variation in vegetation and environment conditions will be experienced.

A brightness temperature (Tb) downscaling algorithm based on the synergy between active and passive microwave observations is tested using airborne data that simulate the Soil Moisture Active Passive (SMAP) mission of the National Aeronautics and Space Administration scheduled for launch in January 2015. While this algorithm has been adopted as the baseline for SMAP, it has only been tested on a limited variety of land uses and vegetation types. Consequently, this study evaluates the SMAP active/passive downscaling algorithm using data with varied conditions. The SMAP experiment conducted in Australia has been used for this purpose. The algorithm was applied over several 9 km × 9 km pixels with different land covers, so as to evaluate the accuracy of this algorithm under different heterogeneity levels. Brightness temperatures were downscaled from 9 to 3 km (approximating the resolution ratio of SMAP downscaling approach) across nine days of data. Results show that the root-mean-square error of Tb in grassland could meet the 2.4-K target accuracy of SMAP, while in cropping, it was 2 K higher than the target. The influence from water bodies was also assessed and confirmed to have a significant impact if not removed prior to downscaling.

NASA's Soil Moisture Active Passive (SMAP) mission will provide a ~10-km resolution global soil moisture product with a 2-3-day revisit by exploiting the synergy between active and passive observations. However, soil moisture downscaling techniques required to exploit this synergy have not yet received extensive testing, being limited to mostly synthetic data. Consequently, airborne field campaigns such as the SMAP Experiments (SMAPEx) have been designed to provide experimental data to fill this gap. The objective of this study is to assess the reliability of SMAP prototype data stream derived from airborne observations, with the aim of providing a simulated SMAP data set for prelaunch algorithm development of SMAP. Specifically, the reliability of incidence-angle normalization and spatial resolution aggregation for airborne observations was assessed for this purpose. The impact of azimuthal angle on active-passive observations was analyzed to assess the potential influence of SMAP rotating antenna on observations. Results showed that the accuracies of angle normalization were ~0.8 dB for active and 2.4 K for the passive observations (1-km resolution), while the uncertainties associated with spatial upscaling were 2.7 dB (150-m resolution) and 2 K (1-km resolution). Although azimuthal signatures associated with the variable orientation of surface features were observed in the high-resolution observations, these tended to be smoothed when aggregating to coarser resolution. As these errors are expected to decrease further at the coarser resolution of SMAP, results suggested that data from SMAPEx can be reliably used to simulate SMAP data for subsequent use in active-passive soil moisture algorithm development.

NASA's Soil Moisture Active Passive (SMAP) mission will carry the first combined spaceborne L-band radiometer and Synthetic Aperture Radar (SAR) system with the objective of mapping near-surface soil moisture and freeze/thaw state globally every 2-3 days. SMAP will provide three soil moisture products: i) high-resolution from radar (~3 km), ii) low-resolution from radiometer (~36 km), and iii) intermediate-resolution from the fusion of radar and radiometer (~9 km). The Soil Moisture Active Passive Experiments (SMAPEx) are a series of three airborne field experiments designed to provide prototype SMAP data for the development and validation of soil moisture retrieval algorithms applicable to the SMAP mission. This paper describes the SMAPEx sampling strategy and presents an overview of the data collected during the three experiments: SMAPEx-1 (July 5-10, 2010), SMAPEx-2 (December 4-8, 2010) and SMAPEx-3 (September 5-23, 2011). The SMAPEx experiments were conducted in a semi-arid agricultural and grazing area located in southeastern Australia, timed so as to acquire data over a seasonal cycle at various stages of the crop growth. Airborne L-band brightness temperature (~1 km) and radar backscatter (~10 m) observations were collected over an area the size of a single SMAP footprint (38 km × 36 km at 35° latitude) with a 2-3 days revisit time, providing SMAP-like data for testing of radiometer-only, radar-only and combined radiometer-radar soil moisture retrieval and downscaling algorithms. Airborne observations were supported by continuous monitoring of near-surface (0-5 cm) soil moisture along with intensive ground monitoring of soil moisture, soil temperature, vegetation biomass and structure, and surface roughness.

Once launched in late 2014, NASA's Soil Moisture Active Passive (SMAP) mission will use a combination of a four-channel L-band radiometer and a three-channel L-band radar to provide high resolution global mapping of soil moisture and landscape freeze/thaw state every 2-3 days. These measurements are valuable to improved understanding of the Earth's water, energy, and carbon cycles, and to many applications of societal benefit. In order for soil moisture and freeze/thaw to be retrieved accurately from SMAP microwave data, prelaunch activities are concentrating on developing improved geophysical retrieval algorithms for each of the SMAP baseline products using data from simulations, from existing satellite missions such as SMOS, and from field campaign data, such as the SMAPEx airborne study in Australia discussed in this paper.