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In runoff generation process, soil moisture plays an important role as it controls the magnitude of the flood events in response to the rainfall inputs. In this study, we investigated the ability of a new era of satellite soil moisture retrievals to improve the Soil & Water Assessment Tool (SWAT) daily discharge simulations via soil moisture data assimilation for two small (< 500 km(2)) and hydrologically different catchments located in Central Italy. We ingested 1) the Soil Moisture Active and Passive (SMAP) Enhanced L3 Radiometer Global Daily 9 km EASE-Grid soil moisture, 2) the Advanced SCATterometer (ASCAT) H113 soil moisture product released within the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) which has a nearly daily temporal resolution and sampling of 12.5 km, and 3) a fused ASCAT/Sentinel-1 (S1) satellite soil moisture product named SCATSAR-SWI with temporal and spatial sampling of 1 day and 1 km, respectively into SWAT hydrological model via the Ensemble Kalman Filter (EnKF). Different configurations were tested with the aim of exploring the effect of the hydrological regime, the land use conditions, the spatial sampling and the revisit time of the products (which controls the amount of available data to be potentially ingested). Results show a general improvement of SWAT discharge simulations for all products in terms of error and Nash Sutcliffe efficiency index. In particular, we found a relatively good behavior of both the active and the passive products in terms of low flows improvement especially for the catchment characterized by a higher baseflow component. The benefit of the higher spatial resolution of SCATSAR-SWI obtained via S1 over ASCAT was small, likely due to very challenging areas for the S1 retrieval. Eventually, better performances were obtained for the passive product in the more forested catchment. With the aim of exploring the benefit of having more frequent satellite soil moisture observations to be ingested, we tested the performance of the ASCAT product with a reduced temporal sampling obtained by temporally matching ASCAT observations to that of SMAP. The results show a significant reduction of the performance of ASCAT, suggesting that the correction frequency (due to the higher number of observations available) for small catchments is an important aspect for improving flood forecasting as it helps to adjust more frequently the pre-storm soil moisture conditions.

A key success criterion for any long-term ESDR (Earth System Data Record) is how well it retains internal consistency upon input from multiple data sources. Our approach to achieving internal consistency of a soil moisture ESDR is based on mature data records derived from established cross-calibration procedures leveraged from the GPM mission. The X-band soil moisture estimates in our soil moisture ESDR will be derived from the GPM Level 1C Common Calibrated Brightness Temperatures (GPMXCAL, Biswas et al., 2014). This product suite contains cross-calibrated brightness temperatures from all radiometers considered in the GPM constellation using the GMI as the calibration reference. The double-difference cross-calibration procedures deployed in the GPMXCAL development have been extensively published, evaluated and refined throughout the development and operation of GPM (Berg et al., 2016). The result is a stable and consistent dataset among radiometers (GMI, AMSR2, WindSat, AMSR-E, and TMI) whose X-band cross-calibrated brightness temperatures cover a period of more than two decades (1997–present), providing the basis for producing a long-term consistent multi-satellite soil moisture ESDR. We then apply the same set of inversion algorithms, physical models, and ancillary data to the cross-calibrated brightness temperatures above. This gives us another means to reinforce ESDR consistency because for a given frequency, cross-calibrated brightness temperatures, regardless of their underlying sources, are all now subject to the same analytical inversion procedures to produce soil moisture estimates.

The Twente region in the east of the Netherlands has a network with twenty soil monitoring stations that has been utilized for validation of the Soil Moisture Active/Passive (SMAP) passive-only soil moisture products. Over the period from April 2015 until December 2018, seven stations covered by the SMAP reference pixels have fairly complete data records. Spatially distributed soil moisture simulations with the Dutch national hydrological model have been utilized for the development of upscaling functions to translate the spatial mean of point measurements to the domain of the SMAP reference pixels. The native and upscaled spatial soil moisture means have been adopted as in situ references to assess the performance of the SMAP i) Single Channel Algorithm at Horizontal Polarization (SCA-H), ii) Single Channel Algorithm at Vertical Polarization (SCA-V), and iii) Dual Channel Algorithm (DCA) soil moisture estimates. In the case of the Twente network it was found that the SCA-V soil moisture retrieved SMAP observations collected in the afternoon had the best agreement with the in situ references leading to an unbiased Root Mean Squared Error (uRMSE) of 0.059 m3 m−3. This is larger than the mission target accuracy of 0.04 m3 m−3, which can be attributed to large over- and underestimation errors (> 0.08 m3 m−3) in particular at the end of dry spells and during freezing, respectively. The strong vertical dielectric gradients associated with rapid soil freezing and wetting causes the disparity in soil depth characterized by SMAP and in situ that leads to the large mismatches. Once filtered for frozen conditions and antecedent rainfall the uRMSE improves to 0.043 m3 m−3.

Soil Moisture Active Passive (SMAP) mission and the Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40° incidence angle. The brightness temperatures from the two missions are close to each other but SMAP observations show a warmer TB bias (about 0.64 K: V pol and 1.14 K: H pol) as compared to SMOS. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between their soil moisture products. The reprocessed constant-angle SMOS brightness temperatures (SMOS-SMAP) were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product has an increased global revisit frequency (1 day) and period of record that is unattainable by either one of the satellites alone. The SMOS-SMAP soil moisture retrievals compared with in situ observations show a retrieval accuracy of less than 0.04 m3/m3. Results from the development and validation of the integrated soil moisture product will be presented.

The potentiality of Signals of Opportunity (SoOp) over land can be investigated by advanced forward and inverse modeling and simulation tools to provide viable measurements for Earth science data products over land. This research investigates various inversion techniques that can leverage SoOp sources for land-based Earth science measurements by applying them to simulated soil moisture profiles over bare- and vegetated- soils. Forward modeling is accomplished using Mississippi State University’s Signals of Opportunity Coherent Bistatic Scattering Model (SCoBi), a new, open-source electromagnetic scattering model that can determine coherent received signals at a receiving antenna through application of Maxwell’s equations at discrete scattering soil layer boundaries in conjunction with the distorted Born approximation to describe vegetation propagation and scattering. The results of the forward model are used in various inverse methods to investigate the potentiality of using multiple SoOp sources for Soil Moisture Profile (SMP) retrieval. Multiple SMPs are analyzed by SCoBi to determine the sensitivity of soil moisture variation to SoOp transmitter characteristics such as polarization and elevation angle. Simultaneously, SoOp measurements conducted at Purdue University’s Agronomy Center for Research and Education (ACRE) are used to determine the impact that changes in both physical SMPs and vegetation canopies have on the scattered SoOp. The characteristics of the scattering surfaces, vegetation, and SMPs at the ACRE facility are modeled within SCoBi to observe patterns and relationships captured in reflectivity measurements that are caused by vegetation growth periods as well as rain and drought effects manifested by changing SMPs.

The SMAP (Soil Moisture Active/Passive) satellite provides global soil moisture (SM) estimates that can be used for scientific research and applications (such as the hydrological cycle, agriculture, ecology, and land atmosphere interactions). Currently, SMAP provides the enhanced radiometer-only SM product (L2SMP) at 9 km grid resolution. However, this spatial resolution is still not enough to satisfy the needs of some studies that require a finer spatial resolution SM product, particularly in agricultural and watershed applications. This study applied a downscaling algorithm to the SMAP 9 km SM product to produce a 1 km resolution over the CONUS (Contiguous United States). The downscaling algorithm is based on the relationship between temperature change and SM modulated by Normalized Difference Vegetation Index (NDVI) of a given time period. This relationship was modeled using variables derived from NLDAS (North America Land Data Assimilation System) and NASA’s LTDR (Land Long Term Data Record) between 1981 - 2018. The algorithm was implemented uses the 1 km MODIS Aqua LST (Land Surface Temperature) product. The downscaled SMAP 1 km SM was validated using in situ SM measurements from the ISMN (International Soil Moisture Network). The validation metrics show an improved overall accuracy of the downscaled SM.

In this paper, we explore a time series approach to using the tau-omega (τ-ω) model to retrieve vegetation water content (kg/m2) with minimal use of ancillary data. Analytically, this approach calls for nonlinear optimization in two steps. First, multiple days of co-located brightness temperature observations are used to retrieve the effective vegetation opacity, which incorporates the combined radiometric and polarization effects of surface roughness and vegetation opacity. The resulting effective vegetation opacity is then used to retrieve vegetation water content to within a gain factor α and an offset factor β. By using a climatological vegetation water content ancillary database as the one adopted in the development of the SMAP standard and enhanced soil moisture products, α and β can be determined globally using the annual minimum and annual maximum of vegetation water content. The resulting values of α and β can then be used to reconstruct the retrieved vegetation water content. Formulation, assumptions, and limitations of this approach are presented alongside the preliminary global retrieval of vegetation water content using one year (2016) of SMAP brightness temperature observations.

SigNals of Opportunity: P-band Investigation (SNOOPI) will be the first on-orbit demonstration of remote sensing using Signals of Opportunity (SoOp) in P-band (240-380 MHz). P-band SoOp has the potential for spaceborne remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE), two variables identified as priorities in the 2017-2027 Decadal Survey for Earth Science and Applications from Space. P-band is needed to penetrate through dense vegetation and into the root zone. SNOOPI will provide inspace validation of both the technique of P-band SoOp and a science instrument prototype. This is a necessary risk-reduction step on the path to a science mission, which will verify important assumptions about reflected signal coherence, robustness to the RFI environment, and our ability to capture and process the reflected signal from orbit. SoOp observations will be used to estimate the complex reflection coefficient over various land surface conditions. These will be used to verify models and show that P-band SoOp can meet working requirements for future RZSM and SWE missions. The SNOOPI instrument design builds upon the heritage of a low noise front end (LNFE), developed from an airborne demonstrator, and a digital back end (DBE) evolved from the Cion, TriG and Blackjack GPS receivers. Success with SNOOPI will retire the critical risks associated with a P-band SoOp satellite instrument and exit at TRL-7. Not only would this instrument enable direct measurements of RZSM and SWE which are not presently possible, it’s size, weight, power and cost (SWaP-C) would also be orders of magnitude smaller than comparable monostatic radars due to the re-utilization of existing, powerful, anthropogenic signals.

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.

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.

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/m3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

International audience; The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. The commissioning phase ended in May 2010. Subsequently, the satellite has been in operation for over six years while the retrieval algorithms from Level 1 (L1) to Level 2 (L2) underwent significant evolutions as knowledge improved. Moreover, other approaches for retrieval at L2 over land were investigated while Level 3 (L3) and Level 4 (L4) were initiated. In this paper, these improvements were assessed by inter-comparisons of the current L2 (V620) against the previous version (V551) and new products (using neural networks referred to as SMOS-NN) and L3 (referred to as SMOS-L3). In addition, a global evaluation of different SMOS soil moisture (SM) products (SMOS-L2, SMOS-L3, and SMOS-NN) was performed comparing products with those of model simulations and other satellites. Finally, all products were evaluated against in situ measurements of soil moisture (SM). To achieve such a goal a set of metrics to evaluate different satellite products are suggested.The study demonstrated that the V620 shows a significant improvement (including those at L1 improving L2) with respect to the earlier version V551. Results also show that neural network based approaches can often yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved through the significant reduction of RFI sources in several areas of the world. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.

An ensemble Kalman Filter-based data assimilation framework that links a crop growth model with active and passive (AP) microwave models was developed to improve estimates of soil moisture (SM) and vegetation biomass over a growing season of soybean. Complementarities in AP observations were incorporated to the framework, where the active observations were used to optimize surface roughness and update vegetation biomass, while passive observations were used to update SM. The framework was implemented in a rain-fed agricultural region of the southern La-Plata Basin during the 2011–2012 growing season, through a synthetic experiment and AP observations from the Aquarius mission. The synthetic experiment was conducted at a temporal resolution of 3 and 7 days to match the current AP missions. The assimilated estimates of SM in the root zone and dry biomass were improved compared to those from the cases without assimilation, during both 3- and 7-day assimilation scenarios. Particularly, the 3-day assimilation provided the best estimates of SM in the near surface and dry biomass with reductions in RMSEs of 41% and 42%, respectively. The absolute differences of assimilated LAI from Aquarius were $ compared to the MODIS LAI indicating that the performance of assimilation was similar to the MODIS product at a regional scale. This study demonstrates the potential of assimilation using AP observations at high temporal resolution such as those from soil moisture active passive (SMAP) for improved estimates of SM and vegetation parameters.

L-band satellite remote sensing is one of the most promising techniques for global monitoring of soil moisture (SM). In addition to soil moisture and ocean salinity (SMOS) SM products, another global SM product has been developed using Aquarius, which is the first operational active/passive L-band satellite sensor. The spatial resolution of Aquarius SM products is about 100 km, which presents more challenges to the ground-based validation. This study explores approaches to validate and evaluate the Aquarius SM products in terms of their spatial and temporal distributions, through synergistic use of in situ measurements and model products from the global land data assimilation system (GLDAS). A dense soil moisture/temperature monitoring network over the central Tibetan plateau (CTP-SMTMN) and sparse stations from the soil climate analysis network (SCAN) over United States are used for the reliability assessment of Aquarius SM products. Results show that the Aquarius SM captures the spatial–temporal variability of CTP-SMTMN reference dataset with an overall RMSD of $0.078\;{\text{m}^3}\cdot{\text{m}^{ - 3}}$ and correlation coefficient of 0.767. The comparison results with reference to SCAN datasets suggest that the RMSD can reach to the target value of $0.04\;{\text{m}^3}\cdot{\text{m}^{ - 3}}$ over specific stations, but the impacts from different orbits, seasons, and land cover types are also found to be significant. The comparison between Aquarius retrievals and GLDAS/common land model (CLM) simulations presents a general well statistical agreement with correlation coefficients above 0.5 for most terrestrial areas. These results are considered to support the use of Aquarius SM products in future applications.

A multilayer module is incorporated into the Signals of Opportunity (SoOp) Coherent Bistatic Scattering model (SCoBi) for determining the reflections and propagation of electric fields within a series of multilayer dielectric slabs. This module can be used in conjunction with other SCoBi components to simulate complex, bistatic simulation schemes that include features such as surface roughness, vegetation, antenna effects, and multilayer soil moisture interactions on reflected signals. This paper introduces the physics underlying the multilayer module and utilizes it to perform a simulation study of the response of SoOp-R measurements with respect to subsurface soil moisture parameters. For a frequency range of 100&ndash, 2400 MHz, it is seen that the SoOp-R response to a single dielectric slab is mostly frequency insensitive, however, the SoOp-R response to multilayer dielectric slabs will vary between frequencies. The relationship between SoOp-R reflectivity and the contributing depth is visualized, and the results show that SoOp-R measurements can display sensitivity to soil moisture below the penetration depth. By simulation of simple soil moisture profiles with different wetting and drying gradients, the dielectric contrast between layers is shown to be the greatest contributing factor to subsurface soil moisture sensitivity. Overall, it is observed that different frequencies can sense different areas of a soil moisture profile, and this behavior can enable subsurface soil moisture data products from SoOp-R observations.

The soil moisture (SM) data retrieved from the Soil Moisture Active and Passive (SMAP) satellite are available at a 9 km grid spacing since April 2015. This product can provide valuable information for research and applications in hydrology and other related fields. However, the resolution may be too coarse for applications at catchment or field scale. In this study, an established downscaling methodology, which had a major modification regarding its application on the SMAP 33 km domain, was implemented to develop a 1 km soil moisture product based on the SMAP 9 km data. The algorithm proposed here is based on the thermal inertia principle and developed by modeling the relationship between surface temperature difference and SM for different Normalized Difference Vegetation Index (NDVI) classes. The model functions were established and tuned using data from the NASA’s Land Information System (LIS) North America Land Data Assimilation System (NLDAS) and remotely sensed VISible/InfRared (VIS/IR) reflectance data from Long Term Data Record (LTDR) AVHRR (Advanced Very High Resolution Radiometer) for the growing season months of April-September 1981–2018. These were then implemented using the MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Continental United States (CONUS) domain. Validation activities were carried out using in situ measurements distributed through the International Soil Moisture Network (ISMN). The validation results computed using the 1 km SM data showed that the R2, unbiased RMSE (root mean square error) and bias were improved relative to the 9 km SMAP product by 0.045, 0.018 m3/m3 and 0.001 m3/m3, respectively. The 1 km SM also exhibited a strong time-series autocorrelation. Further accuracy assessment analyses indicated that precipitation might contribute to the uncertainties in both the 9 km SMAP and 1 km downscaled SMAP SM products.

Routine, repeatable radar measurements of soil moisture are expected to be achievable through the use of frequencies with penetration depths that reach the root-zone of given vegetation structures within soil. Due to the size, weight, power, cost, and legal constraints of creating spaceborne radar transmitters, the practice of reutilizing existing satellite navigation/communication systems for spaceborne radar applications is gaining attention from research communities under the title "Signal of Opportunity" (SoOp). The use of SoOp transmitters as a free source of illuminators is expected to be a viable tool for the remote sensing of soil moisture at depths below 5 centimeters. However, the returns of a radar signal can be representative of many types of soil moisture profiles, and the returned radar signals do not indicate the depth of the contained soil moisture. To this end, this paper is intended to better understand the various effects that radar parameters have on a returned radar signal given a known, simple soil moisture profile in order to help determine the feasibility of using multiple (SoOp) transmitters to determine soil moisture profiles. In order to accomplish this, the recently developed SoOp Coherent Bistatic (SCoBi) scattering model is used to determine the relative permittivity of a given soil moisture profile as well as determine the complex reflection coefficients of the incident signals upon the surface.

International audience; After almost 9 years in orbit L band satellite radiometry has demonstrated its impacts and values for a wide range of science and applications. In some cases it has demonstrated its uniqueness for assessing key environmental variables and in many others its high impact.

The SMOS (Soil Moisture and Ocean Salinity) mission, together with other passive microwave based missions (AMSR, SMAP), provides soil moisture estimates at resolutions ranging from 30 to 55 km. These estimates are validated by direct comparison to in situ measurements that typically measure over an area of a few centimeters. There exist a spatial scale mismatch between the satellite (large support) and the in situ measurements (point support), which contributes to the differences observed. Their magnitude depends on the spatial representativeness of the in situ measurements, which varies in time and with the selected location. This communication will show how the spatial scale mismatch evolves through timescales. It is characterized by using modeled, in situ and satellite soil moisture time series. Timescales, from 0.5 to 128 days, are obtained using wavelet transforms and the spatial representativeness is assessed with a new approach that uses wavelet-based correlations (WCor).

Soil Moisture Active Passive (SMAP) mission and the Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2–3 days. SMAP brightness temperature observations were compared with SMOS observations at 40° incidence angle. The brightness temperatures from the two missions are not consistent. SMAP observations show a warmer TB bias (about 1.27 K: V pol and 0.62 K: H pol) as compared to SMOS. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between their soil moisture products. The reprocessed constant-angle SMOS brightness temperatures were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product has an increased global revisit frequency (1 day) and period of record that is unattainable by either one of the satellites alone. Results from the development and validation of the integrated soil moisture product will be presented.

Microwave remote sensing measurements at L-band (~1.2-1.6 GHz) of geophysical parameters such as soil moisture will need to be at higher spatial resolution than current systems (SMOS/SMAP/ Aquarius) in order to meet the requirements of land surface, ocean, and numerical weather prediction models in the near future, which will operate at ~9-15 km global grids and 1–3 km regional grids in the next few years. In order to make progress toward these needed spatial resolutions, advancements in technology are necessary which would lead to improved effective (i.e. equivalent) antenna size. An architecture trade study was conducted to quantitatively define the value and limits of different microwave technology paths, and to select the most appropriate path to achieve the high spatial resolution required by science in the future without sacrificing performance, accuracy, and global coverage.

In passive microwave remote sensing of soil moisture, the tau-omega $(\tau-\omega)$ model has often been used to provide soil moisture estimates at a spatial scale representative of the satellite footprint dimensions. For modeling simplicity, model parameters such as the single scattering albedo $(\omega)$ and vegetation opacity $(\tau)$ that go into the geophysical inversion process are often assumed to be independent of polarizations. Although this absence of polarization dependence can often be justified in special cases as in low-frequency remote sensing or under dense vegetation conditions, it is not a robust assumption in general. Additional model parameterization errors arising from this assumption are possible, leading to degradation in soil moisture estimation accuracy. In this paper, we propose a time series approach to try to resolve the polarization dependence of several $\tau-\omega$ model parameters as well as the temperature bias arising from the ancillary temperature data. The Version 4 of the Soil Moisture Active Passive (SMAP) Level 1B brightness temperature time series observations were used to illustrate the mechanics of this approach, with an emphasis on the comparison between resulting satellite retrieval and in situ data collected at several core validation sites. It was found that this time series approach resulted in significant reduction of dry bias exhibited in the current SMAP passive soil moisture data products, while retaining the same performance in other metrics of the current baseline passive soil moisture retrieval algorithm.

Root zone soil moisture (RZSM) is an essential variable in meteorology, hydrology, and agriculture. A penetration depth sufficient to sense RZSM requires frequencies below about 500 MHz (I- and P-band). Active or passive microwave sensing in these bands presents substantial technical challenges due to antenna size, radio frequency interference (RFI) and competition for spectrum. Bistatic radar using Signal of Opportunity (SoOp) (e.g. digital satellite transmitters) offers an alternative approach, through reutilizing powerful signals already occupying bands allocated for communications. Airborne experiments using 240–270 MHz sources were conducted in October 2016, followed by a campaign using 360–380 MHz from a fixed tower location in an agricultural research site during the 2017 growing season. A new campaign that will also include I-band (137 MHz) is presently being installed in advance of the 2018 season. This paper will summarize activities to support the reduction of data from these campaigns and development of soil moisture profile retrievals.

SMAP (Soil Moisture Active/Passive) and SMOS (Soil Moisture Ocean Salinity) provide soil moisture observations that can be used for studying the global hydrological cycle, agriculture, ecology, and land atmosphere interactions. SMAP provides soil moisture at two grid scales; 36 km (which is close to its native radiometer spatial resolution) and an enhanced grid resolution of 9 km. However, these scales are not compatible with some agricultural and watershed applications that require a higher spatial resolution. This study applied a downscaling algorithm to the SMAP Level-2 radiometer 36 km product and improved the grid resolution to 1 km over the CONUS (Contiguous United States). The downscaling algorithm is built on the thermal inertial relationship between daily temperature change and averaged soil moisture modulated by Normalized Difference Vegetation Index (NDVI). The average soil moisture and thermal inertia model functions were developed by using data from NLDAS (North America Land Data Assimilation System) and LTDR (Land Long Term Data Record) for 1981 – 2016. The algorithm is applied with the 1 km MODIS Aqua LST product and the downscaled SMAP 1 km soil moisture was validated by in situ soil moisture measurements from the ISMN (International Soil Moisture Network). The validation variables show improved accuracy of the downscaled soil moisture.

The Soil Moisture Active Passive (SMAP) satellite mission launched in January 2015 provides worldwide soil moisture (SM) monitoring based on L-band brightness temperature ( $T_{B}^{\mathrm{ p}}$ ) measurements at horizontal ( $\text{T}_{B}^{\mathrm{ H}}$ ) and vertical ( $T_{B}^{\mathrm{ V}}$ ) polarizations. This paper presents a performance assessment of SMAP soil emission model and SM retrieval algorithms for a Tibetan desert ecosystem. It is found that the SMAP emission model largely underestimates the SMAP measured $T_{B}^{\mathrm{ H}}$ (≈ 15 K), and the $T_{B}^{\mathrm{ V}}$ is underestimated during dry-down episodes. A cold bias is noted for the SMAP effective temperature due to underestimation of soil temperature, leading to the $T_{B}^{\mathrm{ p}}$ underestimation (>5 K). The remaining $T_{B}^{\mathrm{ H}}$ underestimation is found to be related to the surface roughness parameterization that underestimates its effect on modulating the $T_{B}^{\mathrm{ p}}$ measurements. Further, the topography and uncertainty of soil information are found to have minor impacts on the $T_{B}^{\mathrm{ p}}$ simulations. The SMAP baseline SM products produced by single-channel algorithm (SCA) using the $T_{B}^{\mathrm{ V}}$ measurements capture the measured SM dynamics well, while an underestimation is noted for the dry-down periods because of $T_{B}^{\mathrm{ V}}$ underestimation. The products based on the SCA with $T_{B}^{\mathrm{ H}}$ measurements underestimate the SM due to underestimation of $T_{B}^{\mathrm{ H}}$ , and the dual-channel algorithm overestimates the SM. After implementing a new surface roughness parameterization and improving the soil temperature and texture information, the deficiencies noted above in $T_{B}^{\mathrm{ p}}$ simulation and SM retrieval are greatly resolved. This indicates that the SMAP SM retrievals can be enhanced by improving both surface roughness and adopted soil temperature and texture information for Tibetan desert ecosystem.

The SMAP microwave radiometer will see its third anniversary of operations on March 31, 2018. Instrument behavior is stable over 33 months of operation to date. The physical temperature of the internal calibration sources varies 0.5°C. The bias current of the noise source drifted by less than 0.1 %. The avalanche breakdown voltage of the noise diode shows 0.01% seasonal variation. The average $\mathrm{NE\Delta T}$ of the radiometer has maintained a stable 1-K value over the period. This stable behavior of the hardware is critical for the consistent calibration. The reflector emissivity was re-estimated using on-orbit data. Use of the new value nearly eliminates bias caused by solar eclipse during the southern hemisphere winter. The radiometer data were recalibrated using, as earlier, global ocean and cold sky views with additional ocean and land views at nadir incidence. The Version 4 recalibrated data exhibit 0.1-K RMS stability over average global ocean and monthly cold-sky views.

The brightness temperature (T B p) observed by the Soil Moisture Active Passive (SMAP) satellite mission is significantly affected by the soil permittivity (ε s), surface roughness and vegetation opacity (τ p). This study assesses the impact of these factors on simulating the SMAP horizontally (p = H) and vertically (p = V) polarized T B p measurements and retrieving the liquid soil water content (θ liq) for both frozen and thawed soils in the typical Tibetan desert and meadow ecosystems. For this investigation, the zero-order approximation of the radiative transfer equations, i.e., τ-ω emission model, is configured with surface roughness and τ p parameterizations adopted by current SMAP soil moisture retrieval algorithms, and the ε s is computed with the four-phase dielectric mixing model that is applicable for both frozen and thawed soils. For the Tibetan desert site, the τ-ω emission model with above configurations underestimates year-round the SMAP T B H measurements (bias > 20 K), while T B V are underestimated during the cold season. Implementation of a new surface roughness parameterization reduces the T B H underestimation, and the improved T B p simulations lead to better θ liq retrievals produced by the single channel algorithm (SCA) using the T B V as well as T B H measurements. The remaining T B H and T B V underestimations are removed by further adopting a new ε s parameterization. For the Tibetan meadow site, the τ-ω emission model overestimates both T B H and T B V during the warm season and underestimates T B H during the cold season when the vegetation is sparse. Implementation of the new surface roughness parameterization reduces the T B H underestimation, and further the T B p overestimation is mitigated by adopting a new τ p parameterization derived from a discrete radiative transfer model previously developed and tested for the same site. The in-situ measured θ liq dynamics are better captured by corresponding retrievals for both frozen and thawed soils with implementation of the new surface roughness and τ p parameterizations, which reduces the unbiased RMSEs by more than 40%. The parameterizations developed in this study are useful to provide consistent and reasonable T B p simulations and θ liq retrievals over the Tibetan Plateau for both frozen and thawed soils based on both SMAP T B H and T B V measurements.

Launched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones. Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAP/Sentinel-1 Active-Passive Soil Moisture Product in April 2017. This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9 km grid instead of a 36 km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (T(A)) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated T(A) data then go through various correction/calibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (LiC_TB_E). The LiC_TB_E product, posted on a 9 km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on in situ data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and in situ data were performed for the duration of April 1, 2015 - October 30, 2016. It was found that the performance of the enhanced 9 km L2_SM_P_E is equivalent to that of the standard 36 km L2_SM_P, attaining a retrieval uncertainty below 0.040 m(3)/m(3) unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized T(B) channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P.

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 m 3 /m 3 at 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 m 3 /m 3 . 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.

Soil moisture plays an integral role in multi-scale hydrologic modeling, agricultural decision analysis, climate change assessments, and drought prediction/prevention. The broad availability of soil moisture estimates has only occurred within the past decade through a combination of in situ networks and satellite-driven remote sensing. The U.S. Climate Reference Network (USCRN) has provided a nationwide in situ resource since 2009. The Advanced Microwave Scanning Radiometer (AMSR-E), launched in 2002, is one of the satellite products available for comparison, but there are a limited number of years where the data records overlap. This study compares the results of modeled historical soil moisture estimates derived using USCRN precipitation data to the remotely sensed estimates provided by the AMSR-E satellite between 2002 and 2011. First, this work assesses the calibrated model's similarity to in situ estimates. Next, the model estimates and in situ measurements are shown to perform comparably well against the AMSR-E satellite product, suggesting that it may be possible to utilize modeled estimates at times and locations where satellite estimates are unavailable and further extend the soil moisture record spatially and temporally.

Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology. Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011–June 2013 ( $\hbox{rmse}=0.031\ \hbox{m}^{3}/\hbox{m}^{3}$ , $\hbox{Bias} = -0.007\ \hbox{m}^{3}/\hbox{m}^{3}$ , and $R = 0.855$ ) . This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive–active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.

Soil Moisture and Ocean Salinity (SMOS) satellite observations offer a unique resource for near-surface soil moisture retrievals. It could provide multi-angular microwave emission signals at L-band and thus provide an entree to infer soil moisture and roughness for bare soil. In this study, a simple model for simulating the L-band microwave emission from exponentially correlated surface was developed and implemented into soil moisture retrieval algorithm. The approach was based on the parameterization of an effective roughness parameter in relation with surface roughness variables (root mean square height and correlation length) and incidence angle. The parameterization was developed on a large set of simulations by a physical model of the advanced integral equation model (AIEM) over a wide range of geophysical properties. This methodology was then implemented using SMOSREX observations to estimate both soil roughness and surface soil moisture content. Results indicated the model developed in this paper can be very useful in understanding the effects of surface roughness on microwave emission and soil moisture retrieval algorithms from L-band passive microwave observations.

Soil moisture ocean salinity (SMOS) mission has been providing L-band multiangular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations also allow for the retrieval of additional parameters beyond soil moisture, but these are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio-frequency interference (RFI) issues and aliasing at lower look angles increase the uncertainty of observations and thereby affect the soil moisture retrieval that utilizes observations at specific angles. In this study, we proposed a two-step regression approach that uses a mixed objective function based on SMOS L1c data products to refine characteristics of multiangular observations. The approach was found to be robust by validation using simulations from a radiative transfer model, and valuable in improving soil moisture estimates from SMOS. In addition, refined brightness temperatures were analyzed over three external targets: Antarctic ice sheet, Amazon rainforest, and Sahara desert, by comparing with WindSat observations. These results provide insights for selecting and utilizing external targets as part of the upcoming soil moisture active passive (SMAP) mission.

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.

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.

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.

Soil moisture at high spatial resolution is required for various land processes related studies. However, currently the resolution of passive microwave retrieved soil moisture is low. To solve this problem, a soil moisture disaggregation algorithm based on thermal inertia relationship between daily temperature change and average soil moisture modulated by vegetation conditions has been formulated. This algorithm was applied to the SMAP (Soil Moisture Active/Passive) to produce the 1 km downscaled soil moisture over the SMAPVEX15 (SMAP Validation Experiment 2015). The disaggregated soil moisture has been compared to in situ observations and the results of this approach are very encouraging.

In this study, a data-fusion algorithm is developed for estimation of high-resolution brightness temperatures (T B ) at 1km from Soil Moisture Active Passive (SMAP) fine-grid T B product at 9km. It uses image segmentation to spatio-temporally cluster the study region based on meteorological and land cover similarity, followed by a support vector machine based regression that computes the value of the high-resolution T B at all pixels. High resolution remote sensing products such as land surface temperature, normalized difference vegetation index, enhanced vegetation index, precipitation, soil texture, and land-cover were used for disaggregation. The algorithm was implemented in Iowa, United States, from May to September 2016, and compared with the field observations of T B from Microwave Water and Energy Balance Experiment conducted as a part of the Soil Moisture Active Passive Validation Experiment (SMAPVEX16-MicroWEX). Additionally, they were also compared with the Sentinel downscaled SMAP T B at 1km. High resolution soil moisture is subsequently derived from high resolution T B using inverse models.

The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide well-calibrated in situ soil moisture measurements within SMAP product grid pixels for diverse conditions and locations. The estimation of the average soil moisture within the SMAP product grid pixels based on in situ measurements is more reliable when location specific calibration of the sensors has been performed and there is adequate replication over the spatial domain, with an up-scaling function based on analysis using independent estimates of the soil moisture distribution. SMAP fulfilled these requirements through a collaborative Cal/Val Partner program. This paper presents the results from 34 candidate core validation sites for the first eleven months of the SMAP mission. As a result of the screening of the sites prior to the availability of SMAP data, out of the 34 candidate sites 18 sites fulfilled all the requirements at one of the resolution scales (at least). The rest of the sites are used as secondary information in algorithm evaluation. The results indicate that the SMAP radiometer-based soil moisture data product meets its expected performance of 0.04 m 3 /m 3 volumetric soil moisture (unbiased root mean square error); the combined radar-radiometer product is close to its expected performance of 0.04 m 3 /m 3 , and the radar-based product meets its target accuracy of 0.06 m 3 /m 3 (the lengths of the combined and radar-based products are truncated to about 10 weeks because of the SMAP radar failure). Upon completing the intensive Cal/Val phase of the mission the SMAP project will continue to enhance the products in the primary and extended geographic domains, in co-operation with the Cal/Val Partners, by continuing the comparisons over the existing core validation sites and inclusion of candidate sites that can address shortcomings.

The NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide 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 Level 2 radiometer-only soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36-km Earth-fixed grid using brightness temperature observations from descending passes. This paper provides the first comparison of the validated-release L2_SM_P product with soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS), Aquarius, Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) missions. This comparison was conducted as part of the SMAP calibration and validation efforts. SMAP and SMOS appear most similar among the five soil moisture products considered in this paper, overall exhibiting the smallest unbiased root-mean-square difference and highest correlation. Overall, SMOS tends to be slightly wetter than SMAP, excluding forests where some differences are observed. SMAP and Aquarius can only be compared for a little more than two months; they compare well, especially over low to moderately vegetated areas. SMAP and ASCAT show similar overall trends and spatial patterns with ASCAT providing wetter soil moistures than SMAP over moderate to dense vegetation. SMAP and AMSR2 largely disagree in their soil moisture trends and spatial patterns; AMSR2 exhibits an overall dry bias, while desert areas are observed to be wetter than SMAP.

The Soil Moisture Active Passive (SMAP) mission provides a global surface soil moisture (SM) product at 36-km resolution from its L-band radiometer. While the coarse resolution is satisfactory to many applications, there are also a lot of applications which would benefit from a higher resolution SM product. The SMAP radiometer-based SM product was downscaled to 1 km using Moderate Resolution Imaging Spectroradiometer (MODIS) data and validated against airborne data from the Passive Active L-band System instrument. The downscaling approach uses MODIS land surface temperature and normalized difference vegetation index to construct soil evaporative efficiency, which is used to downscale the SMAP SM. The algorithm was applied to one SMAP pixel during the SMAP Validation Experiment 2015 (SMAPVEX15) in a semiarid study area for validation of the approach. SMAPVEX15 offers a unique data set for testing SM downscaling algorithms. The results indicated reasonable skill (root-mean-square difference of 0.053 m(3)/m(3) for 1-km resolution and 0.037 m(3)/m(3) for 3-km resolution) in resolving high-resolution SM features within the coarse-scale pixel. The success benefits from the fact that the surface temperature in this region is controlled by soil evaporation, the topographical variation within the chosen pixel area is relatively moderate, and the vegetation density is relatively low over most parts of the pixel. The analysis showed that the combination of the SMAP and MODIS data under these conditions can result in a high-resolution SM product with an accuracy suitable for many applications.

The radar vegetation index (RVI) has low sensitivity to changes in environmental conditions and has the potential as a tool to monitor vegetation growth. In this letter, we expand on previous research by investigating the radar response over a wheat canopy. RVI was computed using observations made with a ground-based multifrequency polarimetric scatterometer system over an entire wheat growth cycle. We analyzed the temporal variations of backscattering coefficients for L-, C-, and X-bands; RVI; vegetation water content (VWC); and fresh weight. We found that the L-band RVI was highly correlated with both VWC (r = 0.98) and fresh weight (r = 0.98). Based upon these analyses, linear equations were developed for estimation of VWC (root-mean-square error (RMSE = 0.126 kg m-2)) and fresh weight (RMSE = 0.12 kg m-2). In addition, the results of the wheat study were combined with previous investigations with other crops (e.g., rice and soybean). We found that a single linear relationship between L-band RVI and VWC can be used for all crop types (RMSE = 0.47 kg m-2). These results clearly demonstrate the potential of RVI as a robust method for characterizing vegetation canopies. VWC is a key input requirement for retrieving soil moisture from microwave remote sensing observations. The results of this investigation will be useful for the Soil Moisture Active and Passive mission (2014), which is designed to measure global soil moisture.

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. © 2014.

A ground-based fully polarimetric scatterometer operating at multiple frequencies was used to continuously monitor soybean growth over the course of a growing season. Polarimetric backscatter data at L-, C-, and X-bands were acquired every 10 min. We analysed the relationships between L-, C-, and X-band signatures, and biophysical measurements over the entire soybean growth period. Temporal changes in backscattering coefficients for all bands followed the patterns observed in the soybean growth measurements leaf area index LAI and vegetation water content VWC. The difference between the backscattering coefficients for horizontally transmitted horizontally received HH and vertically transmitted vertically received VV polarizations at the L-band was apparent after the R2 stage DOY 224 due to the double-bounce scattering effect. Results indicated that L-, C-, and X-band radar backscatter data can be used to detect different soybean growth stages. The results of correlation analyses between the backscattering coefficient for specific bands/polarizations and soybean growth data showed that L-band HH-polarization had the highest correlation with the vegetation parameters LAI r = 0.98 and VWC r = 0.97. Prediction equations for estimation of soybean growth parameters from the L-HH were developed. The results indicated that L-HH could be used for estimating the vegetation biophysical parameters considered here with high accuracy. These results provide a basis for developing a method to retrieve crop biophysical properties and guidance on the optimum microwave frequency and polarization necessary to monitor crop conditions. The results are directly applicable to systems such as the proposed NASA Soil Moisture Active Passive SMAP satellite.

The National Aeronautics and Space Administration's (NASA) proposed Soil Moisture Active Passive (SMAP) satellite mission ( ~ 2014) will include a radar system that will provide L-band multi-polarization backscatter at a constant incidence angle of 40 °. During the pre-launch phase of the project, there is a need for observations that will support the radar-based soil moisture algorithm development and validation. A valuable resource for providing these observations is the NASA Jet Propulsion Laboratory Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). However, SMAP will observe at a constant incidence angle of 40 °, and UAVSAR collects data over a wide range of incidence angles (25 °-60°). In this investigation, a technique was developed and tested for normalizing UAVSAR data to a constant incidence angle. The approach is based on a histogram matching procedure. The data used to develop and demonstrate this approach were collected as part of the Canadian Soil Moisture Experiment 2010 (CanEx-SM10). Land cover in the region included agriculture and forest. Evaluation was made possible by the acquisition of numerous overlapping UAVSAR flight lines that provided multiple incidence angle observations of the same locations. Actual observations at a 40° incidence angle were compared to the normalized data to assess performance of the normalization technique. An optimum technique should be able to reduce the systematic error (Bias) to 0 dB and to lower the total root mean square error (RMSE) computed after correction to the level of the initial residual error (RMSEres) present in the data set. The normalization approach developed here achieved both of these. Bias caused by the incidence angle variability was minimized to ~ 0 dB, whereas the residual error caused by instrument related random errors and amplitude fluctuations due to ground variability was reduced to approximately 3 dB for agricultural areas and 2.6 dB for forests; these values were consistent with the initial RMSEres estimated using the un-corrected data. The residual error can be reduced further by aggregating the radar observations to a coarser grid spacing. The technique adequately adjusted the backscatter over the full swath width irrespective of the original incidence angle, polarization, and ground conditions (vegetation cover and soil moisture). In addition to providing a basis for fully exploiting UAVSAR (or similar aircraft systems) for SMAP algorithm development and validation, the technique could also be adapted to satellite radar systems. This normalization approach will also be beneficial in terms of reducing the number of flight lines required to cover a study area, which would eventually result in more cost-effective soil moisture field campaigns.

Verifying the calibration of the SMAP radiometer over land observations is an important mission requirement. Inter-comparison of L-band brightness temperature observations from different satellites (SMAP, SMOS and Aquarius) is a useful tool for radiometer calibration. Brightness temperatures observations made at the same frequency, polarization, incidence angle and coincident in time and location should be consistent with each other. SMAP brightness temperature observations were compared with SMOS observations at 40o incidence angle. The observations from the two satellites were found to be consistent with each other over the entire dynamic range (both ocean and land). The RMSD between the two missions was less than 3 K. The two observations exhibit a strong linear relationship and the observed bias was less than 0.5 K for both polarizations. This bias is within the required target accuracy requirement of the SMAP radiometer (requirement of 1.3 K).

Field experiments have played a critical role in the development and implementation of satellite soil moisture missions. A review of key experiments is presented that includes tower-, aircraft, and satellite-focused efforts conducted over four decades that have supported two dedicated satellite missions; Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active passive (SMAP).