We present a concept for UHF/L-band (0.5–2 GHz) remote sensing of Antarctic ice sheet internal temperature using a highly sparse synthetic aperture radiometer constellation. This concept leverages the relative stability of ice sheet thermal emission over long temporal periods to gradually assemble a collection of array baselines which are jointly transformed to develop large image facets. We formulate a calculation of minimum array complexity based on the desired sensitivity, spatial resolution, and time available for observations. We determine from this calculation that such a system can achieve 1–10-km spatial resolution (significantly finer than the program of record) over monthly to yearly timescales with as few as 10–20 elements; even fewer elements are required for observing only the ice sheet center. The inverse problem of reconstructing image facets from mixed-pointing and mixed-configuration observations is posed using a Fourier domain data constraint with a total variational regularization in the image domain. This approach enables image formation from heterogeneous observations while mitigating artifacts. We present a notional constellation design for three satellites which could accomplish the necessary baseline sampling by rotating the phase and semimajor axis of spacecraft relative positions in planar circular orbits (PCOs). We demonstrate image formation by observing system simulations leveraging predictions of Antarctica’s multiwavelength brightness temperature computed from ice sheet thermomechanical and radiative transfer models.

Microwave remote sensing is a widely used and effective method for observing and understanding various land, ocean, and atmospheric processes. Information on the geophysical variables associated with these processes is typically retrieved through inversion of forward models, which describe the remotely sensed observations as functions of the geophysical variables of the scene. Inversion of the forward model is often an ill-posed problem due to the limited information content of microwave measurements and the complexity of the observed scene. The Bayesian approach provides means to incorporate prior information to reduce the ill-posedness of the problem. Considering temporal information is particularly useful in this context, as the temporal characteristics of geophysical variables can be used as prior information to reduce the ill-posedness. Furthermore, considering the temporal domain is natural due to the sequential nature of remote sensing observations. To incorporate such prior information, we introduce a Bayesian inversion of a time series of geophysical variables from a time series of remote sensing observations. The method is formulated in a general form and is therefore applicable to different remote sensing problems. To demonstrate, we applied the method to Soil Moisture and Ocean Salinity (SMOS) L band brightness temperature measurements to simultaneously retrieve, for the first time, ground permittivity, surface roughness, vegetation optical depth, and scattering albedo over a one-year period at a northern boreal forest site. We compared the retrieved geophysical variables with the ground reference: retrieved and measured ground permittivity agreed with a correlation of 0.91, and retrieved and measured vegetation optical depth agreed with a correlation of 0.68 and the standard deviation of the difference of 0.11. We demonstrated that using the time series method, we could retrieve ground surface roughness and vegetation scattering albedo. Although reference measurements were not available for these variables, the retrieved values were consistent with the ground permittivity and vegetation optical depth. In general, ground surface roughness and vegetation scattering albedo are not retrieved in the nominal case, where data from different satellite overpasses are treated separately. This demonstrates that the time series method enables retrieval of additional information compared to the usual approach.

Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere (NH) is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets. A new framework for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network is presented. This framework defines the landscape FT-cycle retrieval as a time-series anomaly detection problem, considering the frozen states as normal and the thawed states as anomalies. The autoencoder retrieves the FT-cycle probabilistically through supervised reconstruction of the brightness temperature (TB) time series using a contrastive loss function that minimizes (maximizes) the reconstruction error for the peak winter (summer). Using the data provided by the Soil Moisture Active Passive (SMAP) satellite, it is demonstrated that the framework learns to isolate the landscape FT states over different land surface types with varying complexities related to the radiometric characteristics of snow cover, lake-ice phenology, and vegetation canopy. The consistency of the retrievals is assessed over Alaska using in situ observations, demonstrating an 11% improvement in accuracy and reduced uncertainties compared to traditional methods that rely on thresholding the normalized polarization ratio (NPR).

We present the STASIS concept, an innovative approach to developing high spatial resolution maps of Antarctic ice sheet thermal emission at P/L band. Rather than using a large real aperture system, the relative stability of ice sheet temperature over time implies that a sparse array system would be able to gradually build up spatial frequency sampling and generate images with 1K sensitivity at 1-10 km spatial resolution over monthly-seasonal time scales. This contrasts with the requirement for full snapshot spatial frequency coverage required by systems for monitoring soil moisture and ocean salinity Sensitivity heuristic calculations are presented, and simulated interferometric observations are generated incorporating a realistic ice sheet thermal emission model.

This study presents an investigation that involves comparing L-band Vegetation Optical Depth (L-VOD) obtained from Global Navigation Satellite System Transmissometry (GNSS-T) against metrics derived from airborne Light Detection and Ranging (LiDAR) data. Both data were collected during the SMAPVEX 2022 campaign in the temperate forests of the northeastern United States, covering Massachusetts and New York. From the LiDAR data, various parameters related to tree characteristics can be extracted, such as tree height, crown diameter and shape, vegetation area density, and woody volume. In this investigation, we initially computed LiDAR point cloud density as a proxy measure of vegetation structure for a given receiver position and the satellite's field of view, comparing it with L-VOD estimates at different positions within the studied forest. Our primary findings reveal a notable correlation between point density and L-VOD, despite the inherent errors in L-VOD estimates and the fact that the number of points may not be the optimal descriptor of the canopy architecture. In this paper, we will explore aforementioned LiDAR derived metrics against the GNSS-T L-VOD estimates to provide insights into the impact of canopy architecture on the L-VOD estimates, determining the specific vegetation layers that influence the measurement.

Greenland ice sheet (GrIS) melting has been a significant concern in the warming climate. Accurate quantification of total surface and subsurface meltwater amount across the pan-Greenland scale is crucial to understanding GrIS mass balance, thus better projecting global sea level rise. We used multi-year L-band observations from the NASA Soil Moisture Active Passive (SMAP) mission to quantify the GrIS surface and sub-surface meltwater amount and examine their spatiotemporal variability. We employed an empirical algorithm to detect surface and subsurface melt events. Then, we applied a physics-based retrieval algorithm to estimate the intensity and physical properties of the melt events. Finally, we validated the retrieval by meltwater derived from a locally calibrated energy balance model with in situ observations from the PROMICE automatic weather station (AWS) network. The retrieval and validation results are presented, which demonstrate generally a good agreement with the meltwater amounts derived from in situ observations.

We reported on the progress of fast hybrid method (FHM) for full- wave simulations of propagation of L-band microwaves in forested environment. For L band, previously we performed full wave simulations of realistic trees initially at 8 meters [1], followed by 13 meters [2]. The progress in this work is at comparisons of the electromagnetic model simulations with SMAPVEX19-22 data: 1) The height of the trees have been extended to 17 meters with the multiple scattering effects of 91 trees in the spatial domain with simulated transmissivity at 0.57 2) the spatial patterns of electric field distribution are simulated with electric field as high as 1.6 that of the incident wave corresponding to 2.56 times the Poynying of the incident waves, and the patterns exhibits gaps and shadows 3) the effects of clustering of trees with gaps show different results from that of uniformly positioned trees and 4) tree structures are varied with examples of trees with two trunks branching out from the main trunk, and the case of tapering trunk radius.

The impact of the initial spatial resolution of soil moisture maps on the quality of downscaled maps by merging with a higher resolution dataset was addressed. Soil moisture maps acquired with airborne sensors in four different campaigns in different climate regions with resolutions of 500 m to 1 km were aggregated to 4-5 km, 8-10 km, 18-20 km and 36-40 km before applying a downscaling algorithm to compute 1 km maps. These maps were compared to the original maps at 1 km resolution. Using different quality metrics, it is shown that the downscaled maps are 30%-75% more accurate when the initial resolution is in the range of 5-10 km with respect to initial resolutions of 36-40 km.

This paper presents a refined Active and Passive (AP) algorithm from the Soil Moisture Active Passive (SMAP) mission, highlighting the progressive enhancements made to the passive algorithm over the years. The primary focus centers on the process of disaggregating coarse brightness temperature (TB) directly measured from the radiometer to attain fine-resolution TB, subsequently enabling the retrieval of soil moisture and vegetation optical depth. Throughout the operational phase of the SMAP SAR instrument, approximately 2.5 months of global SAR backscattering data were acquired simultaneously with TB data. With the imminent launch of the NASA-ISRO Synthetic Aperture Radar (NISAR) mission, the availability of continuous L-band SAR data will see a significant boost. The original SMAP SAR data encompassed four polarizations (VV, HH, HV, and VH), which prompted an examination of three disaggregation combinations: 1) The original SMAP AP algorithm, which utilizes HH, VV, and cross-polarization (X-pol) data (averaged from cross-polarizations). 2) Sole reliance on HH and X-pol data, a configuration that aligns with the capabilities of the NISAR mission, offering global coverage. 3) VV and X-pol data, aiming to provide a more comprehensive analysis. Across these three combinations, similar accuracy was observed at the core study sites, affirming the feasibility of utilizing NISAR HH/HV data exclusively for the AP algorithm. Additionally, this paper also demonstrates both the snapshot method and time-series method for parameter determination and engages in the discussion of their respective advantages and disadvantages.

High-resolution soil moisture plays an important role in irrigation scheduling and agriculture management. A spatial downscaling method based on the OPtical TRApezoid Model (OPTRAM) was proposed to retrieve soil moisture at 500 m spatial resolution by combining SMAP and MODIS observations. This method is less sensitive to cloud cover than the land surface temperature (LST)-based algorithms. The performance of this method was evaluated using soil moisture data derived from aircraft observations of L-band brightness temperature and compared to a typical LST-based downscaling method known as DisPATCh (DISaggregation based on Physical And Theoretical scale Change). The RMSE (Root Mean Squared Error) of the retrieved soil moisture was 0.037 m3/m3for the proposed method, which is lower than the 0.064 m3/m3achieved for the DisPATCh algorithm.

Downscaling of passive microwave derived soil moisture (SM) using thermal-infrared remote sensing data is a common method to obtain higher-resolution SM information. However, these thermal-based downscaling methods are limited by their requirement of daily reparameterization, because thermal infrared data is influenced by both the SM content and weather conditions. To overcome these limitations, this study developed a new method called Downscaling method based on optical Trapezoid model (DespiTe), which improves the spatial resolution of passive microwave derived SM products by using only optical remote sensing data. This method relies on the linear relationship between SM and the Shortwave-infrared Transformed Reflectance (STR), which is less affected by weather conditions. Thus, the DespiTe method needs to be parameterized only once for each area. By establishing statistical relationships between optical parameters (NDVI and STR) and passive microwave derived SM observations, the DespiTe method enables a higher spatial resolution SM estimation to be derived. The downscaling results from DespiTe were evaluated with aircraft and in situ observed SM data acquired during the SMAPEx-4 and SMAPVEX16 field experiments. Moreover, these results were compared with those from DisPATCh, a commonly used thermal-based downscaling method. Comparison against aircraft SM data showed that the DespiTe method achieved an unbiased root mean square error (ubRMSE) of 0.026 m³/m³, which outperformed the DisPATCh method that achieved an ubRMSE of 0.059 m³/m³.

We discuss an approach to measuring high-resolution maps of Antarctic ice sheet temperatures using repeat-pass sparsely sampled microwave interferometry. This approach follows from the inference that the relative invariance of ice sheet temperatures on annual timescales obviates the need for high snapshot sensitivity imposed as a requirement for observing more variable regions of the Earth system with interferometers such as SMOS. Such measurements could hypothetically be conducted with spatial resolutions less than 10 km using a small constellation of satellites. We discuss specifically how modifications to sheet-base geothermal heat flux could manifest as observable thermal signatures and a strategy to form images from a mosaic of multiple heterogeneous sparsely sampled observations, and we conclude with comments on necessary areas for future investigations.

L-band FMCW radar is implemented for monitoring forest dynamics. It took short-term and long-term measurements with an internal calibration system that guarantees stability and precision. The radar data is compared to in-situ measurement, which infers causal relationships between radar backscatter signal and forest physiology index such as tree dielectric. This paper explains the relationship between radar signals and environmental components such as precipitation based on the measurement. The radar demonstrates some interesting observations, for example, trees’ diurnal activity and freeze-thaw process.

Two intense observation periods (IOPs) are included in the Soil Moisture Active Passive (SMAP) Validation Experiment (SMAPVEX) 2022 in the temperate forests of the northeastern US (Massachusetts and New York). Because a sizable portion of the U.S. and the world have non-uniform forest cover at the SMAP resolution scale, the IOPs aim to test the SMAP retrieval in both fully wooded and partially forested instances. Destructive sampling is often used to assess the opacity of the forest canopy, which is intrusive and labor-intensive in forest characterization. To measure vegetative opacity directly utilizing widely accessible Global Navigation Satellite System (GNSS) signals, we have instead developed a GNSS Transmissometry (GNSS-T) approach from a mobile platform (such as a helmet wearable and quadruped ground robot). The created system gathers two simultaneous GNSS readings, one in the unobstructed open sky area and the other under the forest canopy. The difference between the two can yield information on forest transmissivity (water content). That can be used to test the SMAP retrieval methods over wooded areas. In this study, we have processed SMAPVEX’s IOP-1 GNSS-T data at selected sites, including GPS, GLONASS, Beidou and Galileo satellites, and generated forest transmissivity and vegetation optical depth (VOD) heatmaps averaged to different angular bins at both SMAPVEX’22 locations.

Satellite microwave radiometers effectively monitor landscape freeze/thaw (FT) transitions but have difficulty distinguishing soil from other landscape properties, which can lower retrieval accuracy. Here, we applied a deep learning model for soil FT classification driven by daily brightness temperatures (TBs) from AMSR2 and SMAP, and trained on soil (~0-5cm depth) FT observations. The probability of frozen or thawed conditions was derived using a model cost function optimized using observational training data over the Northern Hemisphere (NH) and five year (2016-2020) study period. Results showed favorable accuracy against soil FT observations from ERA5 reanalysis (mean annual accuracy, MAE: 92.7%) and NH weather stations (MAE: 91.0%). Moreover, SMAP L-band (1.41 GHz) TBs provided enhanced soil FT performance over alternative retrievals derived using only AMSR2 inputs. FT accuracy was also consistent across different land covers and seasons. The results provide better soil FT precision to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks.

Recent applications of passive L-band observations from space are summarized for ocean, land surface and cryosphere applications. The main limitation of the measurements performed by the current generation of sensors is the spatial resolution. The need of a mission ensuring the continuation of L-band measurements from space with high spatial resolution (10-15 km) is discussed.

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

The SMAP product provides the soil moisture (SM) computed using three different retrieval algorithms: the single channel H and V algorithms (SCA-H and SCA-V), and the dual-channel algorithm (DCA) which in addition provides the vegetation optical depth (VOD). The roughness model and the roughness parameters play an important role in the determination of the soil moisture and the VOD. In this regard, the SMAP SCA and DCA utilize different approaches to incorporate the effect of roughness. In this work we will summarize those approaches and we will evaluate the effect of the DCA approach on the retrieval of VOD. We will compare the SMAP DCA roughness parameter$h$with topographic parameters such as DEM height, DEM slope, DEM height standard deviation and DEM slope standard deviation.

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

For understanding englacial hydrology and its impact on ice sheet mass balance, observations of the liquid water content (LWC) within the ice sheets are needed. Earlier studies have shown the complementary nature of multi-frequency microwave radiometer measurements to detect subsurface LWC distribution in addition to surface LWC, which is critical for understanding the seasonal melt dynamics of ice sheets. In this study, we used 1.4 GHz brightness temperature (TB) measurements from the NASA Soil Moisture Active Passive (SMAP) satellite, and 6.9, 10.7, 18.9, and 36.5 GHz TB measurements from the JAXA Global Change Observation Mission-Water Shizuku (GCOM-W) satellite to investigate the multi-frequency response at pan-Greenland scale. The melt indications derived at different frequencies show trends consistent with persistent seasonal subsurface melt water and delayed subsurface refreezing of the seasonal melt water. The result suggests that the seasonal subsurface persistent melt water occurrences that are not captured by the high-frequency retrievals are both temporally and spatially very significant.

In this paper, the Numerical Maxwell Model of 3D (NMM3D) full-wave simulation is performed over a corn field using a hybrid method to study the vegetation effect on the microwave. The commercial software of FEKO is used to extract T-matrix of single corn in the first step. Then the calculated T-matrix is combined with Wave Multiple Scattering Theory (W-MST) in the second step to consider the multiple scattering among different plants. The hybrid method is validated with HFSS by solving scattering from 2 corns. A corn field of 25 corn is simulated using the hybrid method and the transmission is calculated and compared with those obtained from the classical radiative transfer model.

The Soil Moisture Active Passive (SMAP) satellite has been developed by NASA to make global soil moisture measurements on the Earth's land surface. It can also distinguish frozen from thawed land surfaces. The SMAP satellite was launched on January 31, 2015, and the science data production began on March 31, 2015. It has both L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna. The SMAP radar failed in July 2015, while its radiometer continues nominal operations. In this paper, the approximately two months of SMAP synthetic aperture radar (SAR) data has been revisited and scrubbed. The SAR bad data (aka outlier) are detected and removed by statistically investigating the time series difference between scatterometer and linearly averaged SAR measurements within the SMAP antenna footprint (∼38 km). It is performed orbit by orbit. The outlier or bad orbits were identified when a data point is more than three scaled median absolute deviations (MAD) away from the median. On average only about 10% or less of all SAR orbits (more than 700), in each polarization, are classified as outliers.

Due to their larger penetration and sensing depth, low frequency microwave measurements have been recently employed to detect ice sheet melt events. In this paper, the response of NASA's SMAP (Soil Moisture Active Passive) L-band measurements to surface melting of the Greenland ice sheet from 2015 through 2021 is investigated. SMAP covers virtually the entire Greenland ice sheet twice a day with its L-band (1.4 GHz) radiometer. The results show that the ice sheet experienced unusually strong surface melting on August 14,2021, which extended the melt area across much of dry snow zone over a period of two days. Moreover, the observational results agree well with model simulations conducted using Glacier Energy and Mass Balance (GEMB) module within the Ice-sheet and Sea-level System Model (ISSM).

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

In preparation for the NISAR mission soil moisture algorithm, this study performs the validation of the European Centre for Medium-Range Weather Forecast (ECMWF) ERA5-Land volumetric soil water (soil moisture) layer product with in-situ measurements from the Soil Moisture Active Passive (SMAP) core validation sites (CVS) for 2015 – 2021. The ERA5-Land soil water layer was also compared against the SMAP-enhanced radiometer soil moisture product (gridded at 9 km) at a global extent. For comparison between the ERA5-Land and the SMAP soil moisture products, a matching temporal dataset was generated from ERA5-Land based on the acquisition time of SMAP for each 9 km grid. In comparison with the CVS in-situ measurement, the ERA5-Land data exhibits an overall ubRMSE of about ~0.05 m3/m3but has high wet bias over most of the sites except for sites in Australia, Denmark, and Argentina. The global comparison of the ERA5-Land soil moisture with the SMAP 9 km gridded product shows an overall wet bias with a high correlation in the tropical and temperate regions. The lowest bias was observed over the desert region but has poor correlation as it doesn’t have enough soil moisture variability. Poor correlation with high bias and high RMSD observed over dense vegetated and tundra regions is possibly due to the inferior performance of the SMAP soil moisture product, which has not been validated for these areas.

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

In this article, a unified framework of vegetation scattering using radiative transfer (RT) theory for passive and active remote sensing of vegetated land surfaces, especially those associated with moderate-to-large vegetation water contents (VWCs), e.g., forest field, is presented. The framework allows for modeling passive and active microwave signatures of the vegetated field with the same physical parameters describing the vegetation structure. RT equations are solved by a numerical iterative approach for both passive and active configurations. This approach allows including higher order scattering, which represents multiple scattering. In fields such as forests with large VWCs, associated with large scattering albedo and optical thickness, multiple scattering effects are critical. In the active iterative approach, cyclical terms are identified and backscattering enhancement is included by doubling contributions from cyclical terms. The method is applied to aspen trees in forest fields to compute the brightness temperatures and backscattering coefficients for passive and active remote sensing configurations, respectively. In the passive configuration, for forest field with VWC of 15 kg/$\text{m}^{2}$, the deviation between the zeroth-order brightness temperature, i.e., the tau–omega model results, and multiple scattering results around 40° observation angle, can be as large as 50 K for vertical polarization and 35 K for horizontal polarization. In the active configuration, the deviation between first-order results, which is identical to the distorted Born approximation, and the multiple scattering results around 40° incidence angle, is about 1.6 dB for VV and 0.7 dB for HH polarization. Multiple scattering is shown to be crucial for accurate forward modeling, especially over forested areas. The proposed approach is thus suitable for vegetation scattering with large VWCs. Furthermore, the proposed model is validated with the passive and active L-band sensor (PALS) acquired in SMAPVEX12 measurements in 2012, which demonstrates the applicability of this model.

The freeze-thaw (FT) status of soil regulates ecological and hydrologic processes and is therefore a vital component of land surface models. This study utilizes a Hidden Markov Model (HMM) to retrieve surface FT status from L-band (SMAP) and Ka-band (AMSR) satellite microwave brightness temperatures in cold-constrained lands north of 45 °N. The HMM, parameterized on a per-gridcell basis such that there are two possible states and emissions probabilities are assumed to be a two-component Gaussian Mixture, produces the posterior probability (a continuous variable between 0 and 1) that the surface is frozen given the radiometer input. HMM classification accuracy, averaged over five core validation networks, is acceptable (Ap > 80 %) when judged against in situ air and soil temperature measurements from individual validation sites and is comparable to that of current FT products that produce a discrete state. Patterns in performance across variable land class, open water fraction, and elevation are assessed from 91 sparse network weather stations within 87 gridcells in the domain. The resulting satellite data record provides a continuous variable estimate of the daily probability of frozen conditions over northern land areas experiencing widespread thawing of permafrost and a shrinking frozen season due to global warming.

Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission augmented with a physical surface scattering model were analyzed to develop a semiempirical model, which consists of three main modeling components for soil moisture, vegetation, and surface roughness. CYGNSS data collected from March 2017 to March 2020 were collocated with the soil moisture data from the Soil Moisture Active Passive (SMAP) mission and climatology vegetation water content (VWC) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. The matchup data were binned as a function of soil moisture, VWC, and incidence angle. The CYGNSS data were calibrated using a coherent reflection equation to obtain an effective reflectivity. The response of CYGNSS effective reflectivity to soil moisture changes is consistent with the change of the Fresnel reflectivity based on Mironov’s soil dielectric constant model used by the SMAP and Soil Moisture Ocean Salinity (SMOS) missions for soil moisture retrieval. The CYGNSS effective reflectivity decreases approximately linearly (in dB) with respect to the NDVI-VWC. The estimated values of vegetation attenuation parameter ($b$) agree with values published in the literature and are corroborated with the estimated values of$b$using the SMAP dual-polarized channel algorithm based on land cover types. A CYGNSS surface scattering map has been derived and reveals a mixed contribution of coherent and incoherent scattering effects and the effects of topography. The semiempirical model, leveraging two of the key modeling functions used by microwave radiometry, will pave the way for a synergistic use of reflectometry and radiometry data for multiparameter retrieval and development of consistent soil moisture products.

The Soil Moisture Active/Passive satellite was launched in 2015 to provide global and continuous maps of land surface soil moisture and freeze-thaw using L-Band microwave radiometry. Even though the 1400-1427 MHz frequency used by SMAP is a protected portion of the spectrum, Radio Frequency Interference (RFI) is still observed that can corrupt the radiometer's measurements. Nine distinct algorithms are implemented as part of SMAP's level 1 processing to detect and filter out RFI contributions. However, any remaining undetected RFI are major concern especially at the locations of cal/val sites used for evaluating soil moisture retrieval performance. This paper presents an analysis of the RFI environment at SMAP cal/val site locations and assesses the impact of RFI on soil moisture retrievals at those locations.

In August 2020, SMAP released a new version of its soil moisture (SM) and vegetation optical depth (VOD) products. In this work, we review the methodology followed by the SMAP regularized dual-channel (DCA) retrieval algorithm. We show that the new implementation generated SM retrievals that not only satisfy the SMAP accuracy requirements but also show a performance comparable to the baseline single-channel algorithm that uses the V polarized brightness temperature (SCA-V). Due to a lack of in situ measurements we cannot evaluate the accuracy of the VOD, but in this work, we will show analysis with the intention of providing an understanding of the VOD product.

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will be augmented with two intensive observation periods (IOP). The first IOP is planned for April 2022 and the other one for July 2022. The IOPs will entail a deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

The NASA Soil Moisture Active Passive (SMAP) mission design includes two instruments that make coincident active and passive measurements in the low frequency microwave L-band range. In the design, the two instruments share the large (6 meter) light-weight mesh rotating reflector and some of the antenna subsystems. The passive radiometer measurements provide measurements that are highly sensitive to surface soil moisture but at coarse resolution. The active radar measurements provide high-resolution measurements but less sensitive to soil moisture variations because of two-way attenuation through the overlying vegetation canopy and more complex rough soil surface scattering. The synergy between the active and passive measurements allows retrieval of surface soil moisture at intermediate scales and with intermediate accuracy. The SMAP radar failed after three months, thus allowing only about three months of active-passive products. The SMAP project switched to using the Copernicus Sentinel 1-A and 1-B C-band SAR measurements for its active-passive product. The disadvantage of the switch-over is the greater vegetation attenuation and more complex rough-surface scattering of C-band when compared to L-band. The advantages are greater resolution of the C-band Synthetic Aperture Radar (SAR). The revisit times are also affected since data from two platforms with different swath widths have to be combined. In this presentation we explore the algorithm issues associated with the switch and compare the products during the period when both the SMAP radar and Sentinel-1 SARs were operating.

To investigate the feasibility of using multi-frequency to further improve the soil moisture retrieval, the recently developed hybrid method is used to perform full-wave simulations of a wheat field at three different frequencies of L-, S-, and C-bands. In the hybrid method, the multiple scattering within a single wheat plant is first captured using the T-matrix based on the full-wave solutions of HFSS. In the second step, the scatterings among different plants are considered using the Foldy-Lax equations of multiple scattering theory (MST). The transmission of microwaves through wheat field at the L-, S- and C-bands are calculated using the hybrid method. Results show that: (1) the transmission obtained from the full-wave simulations is much larger than those computed from the radiative transfer equations (RTE) model, (2) the hybrid method transmission has a weaker frequency dependence than those of the RTE, and (3) the attenuation caused by the vegetation layer would saturate with an increase in frequency.

More than half of the global land area undergoes seasonal freeze/thaw (FT) transitions in spring. Spatial patterns and timing of spring thawing influence eco-hydrological processes and landscape moisture availability over arctic and boreal ecosystems. The seasonal progression of spring thawing coincides with warmer temperatures, snowmelt, and a rapid increase in soil moisture, which initiates the growing season for ecosystem productivity. In this study, we utilize complementary satellite observations to determine the pattern and order of occurrence in landscape thawing, soil moisture increase, and ecosystem productivity that collectively define the eco-hydrological spring onset across Alaska and Northern Canada. Satellite data utilized include landscape FT status from SMAP and AMSR-2, OCO-2 derived solar-induced chlorophyll fluorescence (GOSIF), and gross primary production (GPP) and soil moisture from SMAP. The resulting spring onset maps showed spring thawing as the precursor to growing season onset, indicated by a rapid rise in available soil moisture and GPP. Our results indicated an average spring transition period of$3\pm 2$(SD) weeks between initial landscape thawing and growing season onset. A rapid increase in soil moisture generally followed landscape thawing but occurred before the subsequent seasonal rise in GPP. Spring onset generally occurred earlier in boreal forest (DOY$102\pm 14$) than arctic tundra (DOY$124\pm 22$).

Low frequency microwave measurements have been used to gain insight into what happens deep inside ice sheets for some time now. In this paper, we used a deep neural network to classify each pixel within SMAP radiometer footprints over the Antarctica ice sheet as melt or no-melt. NASA's SMAP mission offers a valuable additional set of observations. The SMAP L-band (1.4 GHz) radiometer retrievals also cover virtually the entire Antarctica ice sheet twice a day. Consistent morning and evening sampling are provided by 6 AM/PM equator-crossings of the satellite ascending and descending polar orbits. The spatial resolution of the instrument is about 40 km. The cross-entropy loss function is used in our network. To make training and test sets, we used air temperature records from available weather stations to distinguish melt and no-melt ice sheet conditions. Our results show that the ice sheet experienced extensive surface melting during the 2015–2016 melt season, and also intensive melting in 2019–2020, particularity on the West Antarctic Ice Sheet.

Low frequency microwave measurements have been used to gain insight into what happens deep inside ice sheets for some time. In this paper, the response of SMAP (Soil Moisture Active Passive) L-band measurements to surface melting of the ice sheet from 2015 through 2019 is investigated. SMAP covers virtually the entire Antarctica ice sheet twice a day with its L-band (1.4 GHz) radiometer. The overpasses center on morning and evening hours as the satellite is on a 6 AM/6PM equator-crossing orbit. The spatial resolution of the instrument is about 40 km. We applied a newly developed geophysical model-based algorithm to detect snow wetness, which can be used as an indicator of melt extent and intensity. It is shown that the ice sheet experienced extensive surface melting during the 2015-2016 melt season (~10% melt extent), and also underwent intensive melting in the 2019-2020 season (median of 0.3% snow wetness), particularity on the West Antarctic Ice Sheet.

Timely, frequent, and complete cropland soil moisture information acquired throughout the growing season is critical for agricultural policy, production, food security, and food prices. The NASA Soil Moisture Active and Passive (SMAP) mission provides a reliable data source for cropland soil moisture assessment. This paper presents Crop-CASMA - a web GIS application tool for cropland soil moisture monitoring and assessment based on SMAP data. This interactive Web service-based GIS application tool enables CONUS SMAP derived soil moisture data visualization, dissemination, and analytics. In this paper, we describe the Crop-CASMA application system architecture, the application implementation, and the data it serves. In addition, we also present a few snapshots of the Crop-CASMA data for cropland soil moisture monitoring. The release of Crop-CASMA greatly enhances the user experience and facilitates using soil moisture data products for crop condition monitoring and decision support.

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will run through 2021 and they will be augmented with two intensive observation periods (IOP). The first IOP will be conducted in April 2021, and a second one in July 2021. The IOPs will see deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

The monitoring of the freeze/thaw dynamic of high-latitude Earth regions is of paramount importance for the study of the carbon cycle and the climate changes. Current approaches essentially rely on the use of active and passive microwave remote sensing, while limited work has been dedicated to study the potential of the Global Navigation Satellite System Reflectometry technique based on spaceborne platforms. In this contribution, reflectivity values derived from the TechDemoSat-1 data have been collected and elaborated, to be compared against the SMAP freeze/thaw product. The proposed analysis indicates a significant seasonal cycle of freeze/thaw state in the calibrated reflectivity, thus opening new perspectives for the bistatic L-band high-resolution satellite monitoring of the freeze/thaw state.

The vegetation layer effects play an important role on microwave remote sensing of soil moisture. The classical Radiative Transfer Equation (RTE) and Distorted Born Approximation (DBA) model assume that the position of scatterers in vegetation is statistically homogeneous in 3D space. Such assumptions are incorrect because the scatterers in vegetation are in clusters and also in the form of extended cylinders. In this paper, we develop a new hybrid method that makes the Numerical Maxwell Model of 3D (NMM3D) full-wave simulation possible for vegetation. A geometry setup is introduced to account for the gap effects and vegetation structure. The T-matrix of a single plant composed of multiple cylinders in a cluster is extracted using Huygen's principle and the vector cylindrical wave (VCW) expansions. Foldy-Lax multiple scattering (FL) equations are used to solve for the transmissivity of the vegetation layer. The convergence and accuracy of the hybrid method are verified using Ansys High Frequency Structure Simulator (HFSS). Transmission through wheat is calculated using the hybrid method and compared with those of RTE/DBA.

Evapotranspiration (ET) is a key link between the global carbon, water and energy cycles. ET generally occurs from soil, vegetation and intercepted precipitation. ET components are commonly estimated using a combination of variables, including meteorology, vegetation, and soil moisture conditions. Although vegetation transpiration has a major effect on global ET variations, soil evaporation can also contribute significant water loss to the atmosphere. This study utilizes satellite derived soil moisture data from the NASA SMAP mission to produce global ET estimates using a modified Penman Monteith algorithm. The global ET results were assessed using other available ET benchmarks. In addition, the ET estimates were evaluated for monitoring spring onset in relation to other complementary satellite observations of vegetation phenology and landscape freeze/thaw metrics. The comparisons between global ET products showed similar latitudinal variation, but with larger differences in tropical rainforests. The spring onset results showed landscape thawing related to rising temperature facilitating the new release of plant-available soil moisture that accompanies a dramatic seasonal rise in both vegetation photosynthesis and ET.

Microwave measurements have been previously used to detect melt events due to their sensitivity to the presence of liquid water in snow. Since NASA's SMAP mission offers a valuable set of low frequency radiometer measurements, SMAP measurements have been used as a tool to detect melt events. SMAP's L-band radiometer also covers virtually the entire Greenland ice sheet twice daily. The overpasses center on morning and evening hours as the satellite is on a 6AM/6PM equator-crossing orbit, and the spatial resolution of the instrument is about 40 km. In this paper, the response of L-band measurements to surface melting of the ice sheet from 2015 through 2019 melt seasons is investigated. It is shown that the Greenland ice sheet experienced an unusually strong melt event at the end of July 2019, which extended the melt area across much of dry snow zone of the ice sheet over a period of two days.

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

Surface roughness parameterization plays an important role in passive microwave soil moisture (SM) retrieval. This article proposes a new formulation for estimating surface roughness. The proposed model incorporates the field-scale (micro) roughness as well as topographic (macro) roughness. The performance of the model is evaluated by inverting the traditional tau-omega model for retrieving SM. The study focuses on the passive active L-band system (PALS) radiometer data collected as a part of two Soil Moisture Active Passive Validation Experiment (SMAPVEX), i.e., SMAPVEX12 (humid Manitoba, Canada) and SMAPVEX15 (semiarid Arizona, USA) with highly different microroughness and macroroughness. The measured surface roughness is observed to increase exponentially with clay fraction (CF). This behavior is minimized with increase in leaf area index (LAI). In the absence of vegetation, the contribution of topography toward surface roughness increases. A higher surface roughness value is estimated for SMAPVEX12, which positively correlate with LAI and CF and negatively correlate with wetness conditions. On the other hand, due to the high topographic variability in SMAPVEX15 region, the contribution of topography (surface curvature) toward total surface roughness is significant. Also, consistently dry SM resulted in high microroughness for SMAPVEX15. Nevertheless, a total surface roughness estimated for SMAPVEX15 region is less than for SMAPVEX12. The surface roughness formulation presented in this study can be extrapolated to any spatial resolution.

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 Soil Moisture Active Passive (SMAP) mission was designed to acquire and combine L-band radar and radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSE of no more than 0.04 m3/m3volumetric accuracy in the top 5 cm for vegetation with water content of less than 5 kg/m2.Currently, a single-channel algorithm that uses the V polarized brightness temperature (SCA-V) is used to retrieve SM satisfying the defined requirements. Even though other alternatives were tested, SCA-V proved to be the best option for the retrieval of SM. In this work, we show that by choosing suitable roughness parameters, the use of two polarizations (H and V), mixed dual-channel algorithm (MDCA), and an additional constraint, regularized DCA (RDCA), not only provides retrieved SM that satisfies the aforementioned requirement but also allows for the retrieval of vegetation optical depth (VOD).

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.

The transmission through vegetation/forest canopy is important for remote sensing of soil moisture. The commonly used vegetation models are distorted Born Approximation (DBA) and Radiative Transfer Equation (RTE). We recently developed Numerical Maxwell Model of 3D (NMM3D) full wave simulations of vegetation/forests. The results of NMM3D show much larger transmission than that of RTE/DBA. A much larger transmission of NMM3D means microwave emission from soil can reach the radiometer, which is different from the conclusion for microwave remote sensing of soil moisture based on RTE and DBA. In this paper, we implement NMM3D based on the scattered field formulation of Foldy-Lax multiple scattering equations (FL). The novelty of this method is that the 3D cylindrical vector wave expansions are used in FL. The correctness of the method is verified. We implement the method on parallel computation using a large number of tall cylinders.

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

This letter presents the results evaluating retrievals of liquid water content (θliq) performed with a zero-order radiative transfer (τ-ω) model under frozen and thawed soil conditions from Soil Moisture Active Passive (SMAP) and ELBARA-III brightness temperature (TBp) measurements collected over a Tibetan meadow ecosystem. A good agreement is found between time series of the SMAP and ELBARA-III measured TBpresulting in a Pearson product-moment coefficient (R) larger than 0.87. Differences noted between the two data sets can be associated with discrepancies in θliqmeasured in the specific footprints, whereby the SMAP measurements are best explained by the in situ θliq. Furthermore, the in situ θliqhas a better agreement with the horizontally polarized SMAP and ELBARA-III measurements (THB ) in the cold season, whereas the vertically polarized measurements (TVB ) are1111better correlated with θliqin the warm season. With the implementation of new vegetation and surface roughness parameterizations for the τ-ω model, the dynamics of in situ θliqis better reproduced by corresponding retrievals for both frozen and thawed soil conditions, leading to the reduction in the unbiased root-mean-square error (ubRMSE) by more than 31% in comparison with these retrievals using SMAP default parameterizations. Notably, the single-channel algorithm configured with the new parameterizations using SMAP TVB measured during the ascending overpass provides the best θliqretrievals with a ubRMSE of 0.035 m3·m-3that is well within the SMAP mission requirements.

The Soil Moisture Active Passive (SMAP) mission currently provides three surface soil moisture products based solely on instrument measurements. The three soil moisture products are: 1) the radiometer-only 36 km gridded; 2) a radiometer-only enhanced product gridded at 9 km; and 3) a high-resolution (3 km) SMAP-Sentinel active–passive product. It is important to validate these released SMAP soil moisture products over various land covers and hydroclimatic domains before they are routinely used in scientific research and applications. This paper evaluates SMAP-based soil moisture products for typical Indian conditions of extreme seasonal variability that leads to changes from very wet to dry soil, especially for the paddy dominated region. The assessment metrics indicate that the enhanced passive-only soil moisture product meets the SMAP accuracy requirement of 0.04 m3/m3during the nongrowing season (NGS) with unbiased root-mean-square error (ubRMSE) values ranging between 0.025 and 0.036 m3/m3. However, this product underperformed during the paddy growing season (GS) with ubRMSE values ranging between 0.063 and 0.097 m3/m3. In addition, the SMAP-Sentinel active–passive soil moisture product shows satisfactory performance during the NGS (ubRMSE, 0.017–0.051 m3/m3), but during the GS, ubRMSE ranged between 0.089 and 0.104 m3/m3. Use of the vegetation water content climatology and low clay fraction in SMAP baseline algorithm (auxiliary database) that mismatched with the actual values may be the possible source of errors and biases in the SMAP soil moisture products. The reported study provides guidelines for the application of enhanced SMAP soil moisture products in India, especially for the tropical region, and provides information that can be used to improve the retrieval algorithm.

Soil moisture retrieval from microwave remote sensing brightness temperatures is in continuous development. In this study, the most recent and latest microwave remote sensing soil moisture products were evaluated against ground-based measurements. We compared, for the first time, the latest versions of SMOS (L2V650 and SMOS-IC V105), SMAP (L3V4), and CCI (V03.2) soil moisture products with respect to ground-based measurements obtained from ISMN (International Soil Moisture Network). Time series were plotted over some sites and it was found that all these products capture well the temporal dynamics over all the sites used in this study. However, CCI was wetter than the in situ measurements over Niger and both SMOS products (IC and L2) and SMAP were drier than the in situ observations over Biebrza site in Poland.

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.

In passive microwave remote sensing of soil moisture, the tau-omega (τ-ω) 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 (ω) and vegetation opacity (τ) 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 τ-ω 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.

In this paper we introduce an approach to enhance the precision of the vegetation parameterization in soil moisture retrieval using passive microwave observations. We present an algorithm that utilizes conventionally defined scattering (S) parameters to represent the propagation of the electromagnetic radiation through the vegetation layer over soil. The S-parameters can be determined with full wave simulations of 3-D Maxwell Equations enabled by recent advances in numerical simulation techniques. Traditional retrieval algorithms have relied on approximation of the radiative transfer equation in order to find simple parameterization for the equation. This results in added uncertainty when the attenuation and scattering within the vegetation layer increases. The presented method provides a way to model the radiative transfer accurately while preserving a reasonable complexity and number of parameters in the algorithm. The results show that the presented method improves the brightness temperature modelling accuracy significantly when vegetation water content reaches about 5 kg/m2, depending on the scattering properties of the vegetation type.

SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. This product is obtained by combining the SMAP radiometer data and the Sentinel-IA and -IB Synthetic Aperture Radar (SAR) data. The approach used for this product draws heavily from the heritage SMAP active-passive algorithm. Modifications in the SMAP active-passive algorithm are done to accommodate the Copernicus Program's Sentinel-IA and -IB multi-angular C-band SAR data. Assessment of the SMAP and Sentinel active-passive algorithm has been conducted and results show feasibility of estimating surface soil moisture at high-resolution in regions with low vegetation density . The beta version of this product is released to public on Nov 1st, 2017. This high resolution (3 km) soil moisture product is useful for agriculture, flood mapping, watershed/rangeland management, and ecological/hydrological applications.

As part of the development of a long-term soil moisture record, we inter-calibrate several low microwave radiometric sensors (SMOS, SMAP, AMSR-E, AMSR2). We use a radiometric simulator over open ocean as a common reference for sensors operating at various frequencies (L-, C- and X-band) and incidence angles. The simulator includes a radiative transfer model for the Earth's emission and the sensors characteristics. It produces antenna temperatures that can be used to account for differences in the incidence and azimuth angle, timing, and frequency. The double difference method is used to inter-calibrate the various sensors.

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

In this paper, we develop a hybrid method combining T matrix of single objects and Fold-Lax multiple scattering theory (FL), for full wave simulations of vegetation/trees. The hybrid method of solving the Maxwell equations consist of off-the-shelf technique for single objects (e.g. HFSS) and newly developed techniques. The newly developed techniques are the three key steps of the hybrid method: (1) extracting the T matrix of each single object, (2) numerical wave transformations and (3) solving the coupled wave interaction equations (i.e. FL) for all the objects. For step (1), we extract the T matrix of a single object by numerical integration with the use of HFSS which is a 3D full-wave electromagnetic field simulation tool. The method of T matrix extraction from HFSS is verified by comparison with the analytical solution of a sphere. The method is applicable to find the T matrix for complicated object where the analytical solution is not available. Then, the wave transformations are performed based on the translation addition theorem. Numerical methods of calculating the transformation coefficients are developed. Finally, the wave interactions among the single objects are accounted for by FL. The results of the hybrid method agree with those of the HFSS brute force method. In comparison, the hybrid method is much more efficient than HFSS for vegetation scattering and applicable to large problems such as full wave simulations of a tree.

The NASA Soil Moisture Active Passive (SMAP) mission has been successfully operated for almost three years. The SMAP freeze/thaw algorithm is based on a seasonal threshold approach. It's important to have a stable and self-consistent freeze and thaw reference that can be applied for multi-year dataset. The three-year long radiometer datasets allow us to reassess the criteria of reference setup and evaluate its stability. In this paper, we first refined the freeze reference requirements and compare three different methods of setting up the thaw reference to minimize the false flags. The original freeze/thaw products is in the polar grid and only cover the region north of 45° N latitude. The limitation is due to lack of enough freezing days in the lower latitude, where the freezing reference cannot be generated. To extend the freeze/thaw product to global region, we combine the single channel algorithm in the lower latitude and southern atmosphere. The global results have been validated through WMO air temperature.

The Soil Moisture Active Passive (SMAP) satellite mission launched in January 2015 provides worldwide soil moisture (SM) monitoring based on L-band brightness temperature (TBp) measurements at horizontal (TBH) and vertical (TBV) 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 THB(≈ 15 K), and the TBVis underestimated during dry-down episodes. A cold bias is noted for the SMAP effective temperature due to underestimation of soil temperature, leading to the TBpunderestimation (>5 K). The remaining TBHunderestimation is found to be related to the surface roughness parameterization that underestimates its effect on modulating the TBpmeasurements. Further, the topography and uncertainty of soil information are found to have minor impacts on the TBpsimulations. The SMAP baseline SM products produced by single-channel algorithm (SCA) using the TBVmeasurements capture the measured SM dynamics well, while an underestimation is noted for the dry-down periods because of TBVunderestimation. The products based on the SCA with TBHmeasurements underestimate the SM due to underestimation of TBH, 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 TBpsimulation 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.

Soil Moisture Active Passive (SMAP) mission and the ESA 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 and have a bias of about 2.7K over land with respect to each other. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between the 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 will have an increased global revisit frequency (1 day) and period of record that would be unattainable by either one of the satellites alone. Results from the development and validation of the integrated product will be presented.

SMAP and SMOS have been successfully providing L-band brightness temperature (Tb) since 2015 and 2011, respectively [1, 2]. Since both missions provide Low Earth Orbit observation at the same frequency, inter-calibration of brightness temperatures measured from these two missions leading to a consistent Tb record is critical in order to have consistent and reliable science datasets. For a radiometer, stability testing and drift monitoring of measured brightness temperature are often performed by comparing measured Tb with estimated Tb using in-situ temperature measurement and/or higher frequency microwave observations. Therefore having one additional reference Tb measured by another radiometer will help characterize and calibrate a radiometer with higher accuracy and benefit both missions.

In this study, a data-fusion algorithm is developed for estimation of high-resolution brightness temperatures (TB) at 1km from Soil Moisture Active Passive (SMAP) fine-grid TBproduct 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 TBat 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 TBfrom 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 TBat 1km. High resolution soil moisture is subsequently derived from high resolution TBusing inverse models.

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.

Spatial and temporal variations in near-surface soil moisture are important to measure for climate studies, numerical weather forecasts, and drought monitoring. Several previous studies have shown success in using ground-reflected Global Navigation Satellite System (GNSS) signals as a form of bistatic radar to sense soil moisture. However, the ability of this type of data to sense soil moisture variations from space is still a nascent field of study. In the past two years, three satellites have been launched that were either designed to capture ground-reflected GNSS signals or have been modified to record these signals. The data provided by these satellites are giving scientists an unprecedented opportunity to investigate their ability to detect changes in Earth's land surface, including but certainly not limited to near-surface soil moisture. This paper will present spaceborne observations of ground-reflected GNSS signals and evaluate their sensitivity to near-surface soil moisture. This sensitivity will be compared to empirical and theoretical sensitivities of monostatic L-band radar measurements to soil moisture. We will also comment on possibilities for retrieval algorithm development, using techniques employed for monostatic radar as a guide.

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.

SMAP project is working on a new and enhanced high-resolution (3km and 1km) soil moisture product. This product will combine SMAP radiometer data and Sentinel-1A and -1B data, and it will use the heritage SMAP active-passive approach. However, modifications in the SMAP active-passive algorithm are done to accommodate the Sentinel-1A and -1B C-band SAR data. Tests of the SMAP and Sentinel active-passive algorithm has been conducted and results show great promise for the high-resolution soil moisture data. The beta version of this product will be released to public in end of the March, 2017. This high-resolution (1 km and 3 km) soil moisture product will be useful for agriculture, flooding, watershed and rangeland management, and ecological and hydrological applications. Specific examples of interest will be shown from the proposed product for the above mention geophysical applications.

SMAP project is working on a new and enhanced high-resolution (3km and 1km) soil moisture product. This product will combine SMAP radiometer data and Sentinel-1A and -1B data, and it will use the heritage SMAP active-passive approach. However, modifications in the SMAP active-passive algorithm are done to accommodate the Sentinel-1A and -1B C-band SAR data. Tests of the SMAP and Sentinel active-passive algorithm has been conducted and results show great promise for the high-resolution soil moisture data. The beta version of this product will be released to public in end of the March, 2017. This high-resolution (1 km and 3 km) soil moisture product will be useful for agriculture, flooding, watershed and rangeland management, and ecological and hydrological applications. Specific examples of interest will be shown from the proposed product for the above mention geophysical applications.

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.

Soil moisture is of great importance to disciplines such as agriculture, hydrology and meteorology. Over the past three decades, passive microwave remote sensing has been demonstrated as a promising tool for global soil moisture estimation and several missions have been launched over the past years. This study focuses on the parametrization of the tau-omega model at L-, C- and X-band for the Yanco site in New South Wales, Australia, and compares the resulting forward-simulated brightness temperatures with two missions: NASA's Soil Moisture Active Passive (SMAP) mission and JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W mission. Preliminary comparison of SMAP and AMSR2 brightness temperatures and forward-simulated brightness temperatures at the Yanco site showed a generally good agreement and higher correlation for the vertical polarization. This is consistent with other studies analyzing the SMAP soil moisture products. Simultaneous calibration of the vegetation parameter b and roughness parameter h was also performed for the L-, C- and X-band data sets, respectively, at both horizontal and vertical polarizations.

We study the scattering of a vegetation canopy consisting of a large number of thin cylindrical scatterers using Numerical Maxwell Model in 3D Simulations (NMM3D). The full wave approach for solving Maxwell equations is based on the Foldy-Lax multiple scattering equations (FL) combined with the Method of Moments for bodies of revolution (BOR). The accuracy of the method FL-BOR is first validated by comparing with the results from the commercial software HFSS for the two and five cylinders' cases. Next, the transmission through a vegetation canopy of cylindrical scatterers is calculated using Monte Carlo simulation where the cylinders, as many as 500, are generated in each realization and the scattering is solved by FL-BOR with exact solutions of the matrix equation. The results of transmission at C-band are compared with those from the distorted Born approximation (DBA) where the attenuation rate {κe}is computed using Foldy's approximation. In NMM3D simulations, the transmission is calculated without the need of defining or calculating the attenuation rate. Two cases are studied: (a) short cylinders where the cylinder lengths are much smaller than the layer thickness and are randomly distributed in 3D in the vegetation layer, and (b) long cylinders where the cylinder lengths are the same as the thickness of the vegetation layer. Case (b) represents several vegetation types and also part of other vegetation types. The results of case (b) show there are significant differences of transmission, as much as 6 dB, from that of DBA.

In this study, the impact of spatial variability due to the heterogeneity of vegetation in the agricultural region on passive microwave signatures available at various scales are explored using the brightness temperature (TB) observed from ground, air, and space. These observations were conducted during a growing season of corn and soybean in South Fork watershed, Iowa, as part of the NASA-Soil Moisture Active Passive Validation Experiment (SMAPVEX16). Both empirical and physically-based microwave emission models are used to understand the effects of vegetation on TBfor corn and soybean using ground-based TBobservations. The modeled TBwill be upscaled based upon the USDA crop layer map to compare with the TBobserved in the coarse scales.

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.

The SMOS, and SMAP retrievals are not only influenced by soil moisture, vegetation but also highly sensitive to surface roughness. The current retrieval algorithms use roughness models and parameterization developed at field scale to estimate soil moisture at satellite footprint. These models ignored large scale roughness features due to differences in elevation, concavity etc., observed within the satellite footprint. In this study, it is hypothesized that the satellite observed surface roughness is contributed by micro-roughness (small-scale roughness) and macro-roughness (large-scale roughness), incorporating the scattering features observed at field and satellite scale. The traditional algorithm is used to retrieve soil moisture for SMAPVEX12 (Soil Moisture Active Passive Validation Experiment, 2012), and SMAPVEX15 the field experiments.

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.

The baseline science objective of the NASA Soil Moisture Active Passive (SMAP) mission is to produce a daily landscape freeze/thaw state for the region north of 45° N latitude with a mean spatial classification accuracy of 80% and 2-3 day average intervals separated by AM and PM overpasses [1]. Following the loss of the SMAP radar in July 2015, radiometer inputs were used to develop a standard freeze/thaw product (L3-FT-P) with relaxed spatial resolution from 3km to 36km. A 9km gridded product (L3-FT-P-E) has been developed by applying enhanced resolution radiometer inputs to the same algorithm. This paper provides an overview of the algorithm development as well as the validation and calibration using in situ observations from both selected core sites and sparse ground station networks.

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 m3/m3for 1-km resolution and 0.037 m3/m3for 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 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 National Aeronautics and Space Administration’s Soil Moisture Active Passive (SMAP) mission Level 4 Carbon (L4C) product provides model estimates of the Net Ecosystem CO2exchange (NEE) incorporating SMAP soil moisture information. The L4C product includes NEE, computed as total ecosystem respiration less gross photosynthesis, at a daily time step posted to a 9-km global grid by plant functional type. Component carbon fluxes, surface soil organic carbon stocks, underlying environmental constraints, and detailed uncertainty metrics are also included. The L4C model is driven by the SMAP Level 4 Soil Moisture data assimilation product, with additional inputs from the Goddard Earth Observing System, Version 5 weather analysis, and Moderate Resolution Imaging Spectroradiometer satellite vegetation data. The L4C data record extends from March 31, 2015 to present with ongoing production and 8–12 day latency. Comparisons against concurrent global CO2eddy flux tower measurements, satellite solar-induced canopy florescence, and other independent observation benchmarks show favorable L4C performance and accuracy, capturing the dynamic biosphere response to recent weather anomalies. Model experiments and L4C spatiotemporal variability were analyzed to understand the independent value of soil moisture and SMAP observations relative to other sources of input information. This analysis highlights the potential for microwave observations to inform models where soil moisture strongly controls land CO2flux variability; however, skill improvement relative to flux towers is not yet discernable within the relatively short validation period. These results indicate that SMAP provides a unique and promising capability for monitoring the linked global terrestrial water and carbon cycles.

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

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

The distorted Born approximation (DBA) combined with the numerical solutions of Maxwell equations (NMM3D) has been used for the radar backscattering model for NASA's Soil Moisture Active Passive (SMAP) mission. The models for vegetated surfaces such as wheat, grass, soybean and corn have been validated with the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) data. In this paper we report progress on development of a consistent model for combined active and passive microwave remote sensing of vegetated surfaces by using the same approach to obtain backscatter and emissivity. The active model DBA/NMM3D is extended to calculate bistatic scattering for each of the three scattering mechanisms: volume, double bounce and surface scattering. Then emissivity is obtained by integration of the bistatic scattering. An advantage of this combined active and passive model is that the same physical parameters of vegetation and soil surfaces are used in both the active model and the passive model. The β parameter that relates backscattering to emissivity is also derived for various vegetated surfaces.

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).

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

Recent theoretical and experimental studies have indicated the feasibility of passive microwave L-band observationsfor observing dry snow cover characteristics, namely snow density in the lower approx.. 10 cm of the snowpack. The sensitivity of L-band emission to snow density is based on the dual influence of refraction and impedance matching on observed brightness temperature with changing effective snow permittivity. The permittivity of pure, dry snow, on the other hand, depends largely on snow density. In this study, we expand the theoretical and experimental results of retrieving dry snow density to passive L-band satellite observations. Such retrievals could be appealing in the context of improving satellite based retrievals of e.g. Snow Water Equivalent (SWE) using other sensors. Retrievals are applied to both multi-angular observations from the ESA SMOS mission, and observations of the NASA SMAP radiometer on a single angle of observation. While in theory the multi-angular approach is preferable, improved RFI mitigation in SMAP provides more spatially and temporally more stable retrievals. The applied dual-parameter retrieval scheme produces also an estimate of ground permittivity; experimental data showed dry snow cover to have a clear influence on ground permittivity retrievals, implicating that even dry snow cover is non-negligible also in retrievals of soil moisture from L-band observations.

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 NASA Soil Moisture Active Passive (SMAP) mission produced a daily landscape freeze/thaw product (L3_FT_A) at 3-km spatial resolution derived from ascending and descending orbits of SMAP high-resolution L-band (1.4 GHz) radar measurements. Following the loss of the SMAP radar in July 2015, coarser (36-km) footprint passive microwave retrievals from the SMAP radiometer were used to derive an alternative daily freeze/thaw product (L3_FT_P). This presentation will provide an overview of the development of both L3_FT products. Validation using in situ observations from core validation sites is used to illustrate differences in the sensitivity of the 3 km radar versus the 36 km radiometer measurements to the landscape freeze/thaw state.

The goal of this study is to assess the performance of the active-passive algorithm for the NASA Soil Moisture Active Passive mission (SMAP) using airborne and ground observations from a field campaign. The SMAP active-passive algorithm disaggregates the coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter (σo) observations. The colocated TB and σoacquired by the aircraft-based Passive Active Land S-band sensor during the SMAP Validation Experiment 2012 (SMAPVEX12) are used to evaluate this algorithm. The estimation of its parameters is affected by changes in vegetation during the campaign. Key features of the campaign were the wide range of vegetation growth and soil moisture conditions during the experiment period. The algorithm performance is evaluated by comparing retrieved soil moisture from the disaggregated brightness temperatures to in situ soil moisture measurements. A minimum performance algorithm is also applied, where the radar data are withheld. The minimum performance algorithm serves as a benchmark to asses the value of the radar to the SMAP active-passive algorithm. The temporal correlation between ground samples and the SMAP active-passive algorithm is improved by 21% relative to minimum performance. The unbiased root-mean-square error is decreased by 15% overall.

Radio-frequency interference (RFI) observations for L-band microwave radiometry during the SMAP Validation Experiment 2012 (SMAPVEX12) airborne campaign are reported in this paper. The soil moisture measurement campaign was conducted in summer 2012 near Winnipeg, MB, Canada, with additional RFI flights over Denver, CO, USA. The Passive Active L-Band sensor (PALS) radiometer of the Jet Propulsion Laboratory was used with a full-bandwidth direct sampling digital backend to measure and store predetection data that is fully resolved in time and frequency. Overviews of SMAPVEX12 and the receiver and digital backend used to collect data are presented, along with the data processing techniques used for RFI detection. Properties of the observed RFI are examined and compared with the results of previous studies. Finally, implications of the results are explained considering current missions such as NASA's Soil Moisture Active Passive Mission.

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

The goal of this study is to assess the performance of the active-passive disaggregation algorithm for the National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission using airborne observations from the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). This algorithm disaggregates the whole domain resolution (around 30 km) radiometer brightness temperature (TB) using the 1.6-km-resolution radar backscatter (σo) observations (both acquired by the aircraft-based Passive Active L- and S-band Sensor), to a medium 6.4-km resolution. The parameters of the disaggregation method are affected by changes in soil and vegetation. Different time windows are studied to assess the best representation of the campaign vegetation growth and senescence processes. The algorithm performance is evaluated by comparing disaggregated and observed TB at the medium resolution. A minimum performance algorithm is also applied where the radar data are withheld. The minimum performance algorithm serves as a benchmark to assess the value of the radar to the SMAP active-passive algorithm.

The National Aeronautics and Space Administration's Aquarius and the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) are satellite missions to make global L-band brightness temperature measurements. Aquarius uses a sophisticated antenna temperature simulator over oceans for the calibration of its brightness temperature measurements. In this investigation, the simulator was adapted to simulate the real aperture antenna temperature measured by the SMOS reference radiometers. It is found that the simulated antenna temperature is very close to the measured value. The analysis shows that the simulated antenna temperature can be utilized for SMOS calibration studies as an additional independent reference, as well as for investigating the consistency between SMOS and Aquarius brightness temperature measurements.

In this letter, it is shown that spaceborne observations made by the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite agreed closely with the Passive Active L-band System (PALS) brightness temperature acquisitions during the Soil Moisture Active Passive (SMAP) Validation Experiment 2012. The difference between the SMOS and PALS measurements was less than 5 K and 6 K for vertical and horizontal polarizations, respectively, over the relatively homogeneous agricultural areas. These values are less than the SMOS subpixel variability determined from the PALS measurement. This result demonstrated that the measurements obtained in the experiment are scalable to spaceborne brightness temperature observations, are representative of the expected SMAP observations, and will be of value in the development of soil moisture algorithms for spaceborne missions.

Despite the continuing efforts to improve existing soil moisture retrieval algorithms, the ability to estimate soil moisture from passive microwave observations is still hampered by problems in accurately modeling the observed microwave signal. This paper focuses on the estimation of effective surface roughness parameters of the L-band Microwave Emission from the Biosphere (L-MEB) model in order to improve soil moisture retrievals from passive microwave observations. Data from the SMAP Validation Experiment 2012 conducted in Canada are used to develop and validate a simple model for the estimation of effective roughness parameters. Results show that the L-MEB roughness parameters can be empirically related to the observed brightness temperatures and the leaf area index of the vegetation. These results indicate that the roughness parameters are compensating for both roughness and vegetation effects. It is also shown, using a leave-one-out cross validation, that the model is able to accurately estimate the roughness parameters necessary for the inversion of the L-MEB model. In order to demonstrate the usefulness of the roughness parameterization, the performance of the model is compared to more traditional roughness formulations. Results indicate that the soil moisture retrieval error can be reduced to 0.054 m3/m3if the roughness formulation proposed in this study is implemented in the soil moisture retrieval algorithm.

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

The Advanced Microwave Scanning Radiometer-EOS (AMSR-E) on Aqua and WindSat on Coriolis instruments have collected multichannel passive microwave data over the global land and oceans since 2002 and 2003, respectively. AMSR-E on Aqua ceased operation in October 2011 due to a malfunction in the antenna scanning mechanism. AMSR-E and WindSat have similar frequencies, bandwidths, polarizations, incidence angles and instantaneous fields of view (IFOVs), but there are some differences in their configurations. The altitudes and local overpass times also differ between the AMSR-E and WindSat sensors. The time series of data from the two instruments have a long period of overlap, which can be used to intercompare and cross-calibrate the instrument data sets taking into account the instrument differences. This would allow retrieval of geophysical parameters using common algorithms that could take advantage of the increased time duration and sampling coverage afforded by combining data from the two sensors. In this paper, we focus on land applications and compare the multichannel data from these two sensors over land. Channels useful primarily for soil moisture and vegetation water content studies (i.e., ~ 6, ~ 10, ~ 18, and ~ 37 GHz at H- and V-pol) are used in the comparisons. To minimize differences caused by surface temperature effects related to local overpass times, only descending passes (with Equator crossing times for AMSR-E of 1:30 a.m. and WindSat 6:00 a.m.) are considered. Homogeneous and temporally stable sites such as Dome-C, Antarctica and the Amazon forest, and a flat and bare region in the Sahara desert are chosen to evaluate similarities and differences among comparable channel observations. Taking into consideration the sensor configurations and geophysical conditions during the descending overpasses, reasonably good agreement is observed between AMSR-E and WindSat measurements over the globe.

A soil moisture retrieval algorithm is proposed that takes advantage of the simultaneous radar and radiometer measurements by the forthcoming NASA Soil Moisture Active Passive (SMAP) mission. The algorithm is designed to downscale SMAP L-band brightness temperature measurements at low resolution ( ~ 40 km) to 9-km brightness temperature by using SMAP's L-band synthetic aperture radar (SAR) backscatter measurements at high resolution (1-3 km) in order to estimate soil moisture at 9-km resolution. The SMAP L-band SAR and radiometer instruments are designed to provide coincident observations at constant incidence angle, but at different spatial resolutions, across a wide swath. The algorithm described here takes advantage of the correlation between temporal fluctuations of brightness temperature and backscatter observed when viewing targets simultaneously at the same angle. Surface characteristics that affect the brightness temperature and backscatter measurements influence the signals at different time scales. This feature is applied in an approach in which fine-scale spatial heterogeneity detected by SAR observations is applied on coarser-scale radiometer measurements to produce an intermediate-resolution disaggregated brightness temperature field. These brightness temperatures are then used with established radiometer-based algorithms to retrieve soil moisture at the intermediate resolution. The capability of the overall algorithm is demonstrated using data acquired by the airborne passive and active L-band system from field campaigns and also by simulated global dataset. Results indicate that the algorithm has the potential to retrieve soil moisture at 9-km resolution, with the accuracy required for SMAP, over regions having vegetation up to 5- kg/m2vegetation water content. The results show a reduction in root mean square error of volumetric soil moisture (40% improvement in the statistics) from the minimum performance defined as the soil moisture retrieved using radiometer measurements re-sampled to the intermediate scale.

A normalized residual scattering index is introduced. This index is based on the relationship between coincident microwave-backscatter and brightness-temperature observations. In this letter, the L-band NRSI is shown to correlate with vegetation cover conditions on a global scale. The interpretation of global observations from the Aquarius satellite is based on Passive Active L-band System airborne data collected during field experiments together with ground truth. The benefit of the method is that it is sensitive to land-cover features affecting both active and passive measurements while being insensitive to surface effects such as soil moisture.

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

The antenna pattern and observation geometry of the SMOS zero-baseline radiometer, which is used as a reference for the SMOS brightness temperature calibration, was applied to Aquarius simulator, which is used as a reference for the Aquarius brightness temperature calibration. In the preliminary analysis, simulations carried out over a three month period show remarkable agreement between the measurements and simulations. This fundamental agreement would indicate that the brightness temperature products of the two missions should be well correlated. Some discrepancies were also found, cause of which seems to be consistent with findings of other studies and can be corrected for.

The mapping of the predominant freeze/thaw state of the landscape is one of the main objectives of the National Aeronautics and Space Administration's proposed Soil Moisture Active Passive (SMAP) mission. This study applies Alaska Ecological Transect (ALECTRA) biophysical network temperature measurements and satellite radar scatterometer data from the Quick Scatterometer (QuikSCAT) to evaluate some of the validation issues regarding the planned SMAP freeze/thaw measurements. Although the QuikSCAT data are acquired at Ku-band frequency, rather than at the L-band frequency of the proposed SMAP instrument, QuikSCAT data do provide a high temporal fidelity over the ALECTRA sites, similar to SMAP. The results of this study show that multiple temperature measurements representative of individual landscape components (soil, snow cover, vegetation, and atmosphere) covering different types of terrain within the satellite field of view are important for understanding the freeze/thaw process and the aggregate radar backscatter response to that process. The backscatter temporal dynamics and relative contribution of the freeze/thaw state of these landscape elements to radar signal vary with land cover, seasonal weather, and climate conditions.

In this paper, we present an analysis of the radiometric performance of the three 1.4-GHz noise injection radiometers of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite. The units measure the antenna temperature, which contributes to the average brightness temperature level of SMOS retrievals. We assess the radiometric resolution of the receivers, the similarity between their measurements, and their thermal stability. For these purposes, we use SMOS measurement data gathered during the first year of the orbital operations of the satellite, which was launched in November 2009. The main results from the analysis are that the units meet the design requirements with a margin. Also, we present a new thermal model for the radiometers to further enhance their stability.

SMOS observations provide an opportunity to develop a testbed for the evaluation of different SMAP algorithm options. The use of real-world global observations will help in the development and selection of different land surface parameters and ancillary observations needed for the soil moisture algorithms. In this study, SMOS observations were used with one soil moisture retrieval algorithm and the results were evaluated using in situ soil moisture measurements. The SMOS soil moisture product, which exploits multiple incidence angle observations, compares well with the ground-based observations (RMSE 0.043 m3/m3(ascending) and 0.047 m3/m3(descending)). The alternative SMAP compatible algorithm also performed well (RMSE 0.040 m3/m3(ascending) and 0.043 m3/m3(descending)). Although preliminary, these initial results are encouraging for the potential of SMAP to meet its required soil moisture accuracy.

The calibration and validation of the freeze/thaw product of NASA's proposed L-band SMAP (Soil Moisture Active and Passive) radar and radiometer mission requires execution of a strategy for characterization of thermal regime of the relevant landscape elements in terms of freeze/thaw state and the associated relationship to the microwave remote sensing signature. The goal of this study is to improve the understanding of the L-band radar backscatter processes over boreal landscapes by comparing ALOS PALSAR high resolution L-band backscatter images with Ku-band backscatter from the SeaWinds QuikSCAT scatterometer and C-, Xand Ka-band brightness temperatures from the Aqua AMSR-E radiometer. The results show that landscape elements driving the L-band backscatter are different from those at higher (Ku-band) frequencies and establishment of an optimal validation strategy for SMAP requires investigation of L-band measurements at spatial scales and temporal fidelity commensurate with landscape freeze/thaw variability.

The mapping of freeze/thaw state of the landscape is one of the main objectives of NASA's upcoming SMAP (Soil Moisture Active and Passive) mission. This study applies ALECTRA (Alaska Ecological Transect) biophysical network and QuikSCAT scatterometer data to evaluate some of the validation issues regarding the SMAP freeze/thaw measurements. Although the QuikSCAT data is at Ku-band frequency, rather than the L-band of the SMAP instrument, the data is utilized due to its uniquely high temporal resolution over the ALECTRA sites. The results show that multiple temperature measurements representative of individual landscape (soil, snow cover, vegetation and atmosphere) elements and spatial heterogeneity within the satellite field-of-view are important for understanding the radar backscatter process and aggregate freeze/thaw signal. The backscatter temporal dynamics and relative contribution of these landscape elements to the freeze-thaw signal varies with land cover type, seasonal weather and climate conditions.

The purpose of the Soil Moisture and Ocean Salinity (SMOS) mission is to measure soil moisture and sea surface salinity (SSS). The measurement of SSS using microwave radiometry requires a very sensitive instrument. In SMOS, the image is formed using the interferometric technique complemented by the average brightness temperature, or zero baseline, to set the absolute level of the image. Therefore, the measurement of the zero baseline is very critical for the success of the mission. In this paper, the radiometric resolution and stability of the radiometers dedicated to the measurement of zero baseline on SMOS are estimated. The results of a measurement campaign carried out in an anechoic electromagnetic compatibility chamber are used. The results show that the zero-baseline radiometers have the potential for relative accuracy better than 20 mK, depending on the integration scenario, satisfying the mission requirement for SSS retrieval.

Polarimetric synthetic aperture interferometric radiometers are analyzed, including the polarization leakage and other front-end non-idealities that can be measured and represented with S-parameters. The analysis utilizes the noise wave concept in order to account for the amplitude and phase properties of the front-end. The method is applied to a model of an entire synthetic aperture interferometric radiometer. The simulations can be used to examine and retrieve requirements for the system parameters. The feasibility of the method is demonstrated and examples of end-to-end simulation results are also given in this paper.

The sensitivity of a radiometer to the brightness temperature variations of the target is one of the critical parameters of any radiometric measurement. The sensitivity is limited by the noise in the measurement and the stability of the instrument. In this study measurements in an anechoic chamber are used to evaluate the sensitivity limit of radiometers, which are the noise injection radiometers of European Space Agency's SMOS (Soil Moisture Ocean Salinity) satellite. The sensitivity is especially important for the measurement of sea surface salinity (SSS), one of the retrieval objectives of SMOS. The methods used include comparisons of the measurements to the physical temperature estimation of the chamber, both in relative and absolute sense, and comparison of different radiometer channels with each other. The analysis demonstrates the reliability of the methods in consistency of both brightness temperature values and physical temperature measurements. Finally, the results show that the noise injection radiometers satisfy the measurement requirement for SSS retrieval.

What is proposed in this paper as a new, novel idea is to use the input port of a complete radiometer receiver as a calibration target instead of a dedicated Active Cold Load (ACL). What is also proposed is to use the input port as a phase calibration target as well. Modern LNA technology has made very low noise figures and return losses possible; both key parameters for a cold and stable ACL. Low noise figure and good matching combined with a typical high isolation of an amplifier makes it possible to use a receiver itself as a cold calibration target. This is beneficial in instruments inherently having (at least) two receivers (pushbroom or polarimetric radiometer for example).

This paper presents a general analysis of correlation measurements in an interferometer or a radiometer based on noise injection and/or switching and measurement of normalized correlations (e.g., imaging synthetic aperture or polarimetric radiometers). A compact unifying notation for denormalizing the measured normalized correlations in the presence of noise injection in one or both of the receiving channels is presented. Technological limitations are also assessed by evaluating the effect of associated approximations. Finally, the approach is validated by experimental results of the measurement and calibration of related front-end nonidealities, namely, the finite isolation of the front-end switch. The methods presented in this paper are illustrated by a thorough analysis of the so-called mixed baselines of microwave imaging radiometer using aperture synthesis, which refer to those baselines which are formed between the regular receivers (light-weight cost-efficient front-end) and the reference radiometers. These baselines require special attention, since the reference radiometers are noise-injection radiometers, which inject noise to the measured signal, whereas the regular receivers are total power receivers.

During the last two decades, the development of synthetic aperture radiometers for remote sensing has been studied intensively. One of the proposed methods for the calibration of such an instrument is the application of a distributed noise injection network. This paper focuses on the origin and effect of errors arising from this methodology. A generalized analytical method to calculate the accumulation of phase and amplitude errors in a distributed noise injection network is presented. This method is then applied to the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), the interferometric radiometer aboard the European Soil Moisture and Ocean Salinity satellite. The effect of the resulting errors to MIRAS' brightness temperature is analyzed. The presented method is applicable also to other interferometric radiometers, whose calibration relies on distributed noise injection.

The calibration of the Soil Moisture and Ocean Salinity (SMOS) payload instrument, known as Microwave Imaging Radiometer by Aperture Synthesis (MIRAS), is based on characterization measurements which are performed initially on-ground prior to launch and, subsequently, in-flight. A good calibration is a prerequisite to ensure the quality of the geophysical data. The calibration scheme encompasses both the spaceborne instrument and the ground data processing. Once the system has been calibrated, the instrument performance can be verified, and the higher level geophysical variables, soil moisture and ocean salinity, can be validated. In this paper, the overall calibration approach is presented, focusing on the main aspects relevant to the SMOS instrument design and mission requirements. The distributed instrument, comprising 72 receivers, leads to a distributed internal calibration approach supported by specific external calibration measurements. The relationship between the calibration data and the routine ground processing is summarized, demonstrating the inherent link between them. Finally, the approach to the in-flight commissioning activities is discussed.

This paper presents the concept and initial breadboarding results of geostationary atmospheric sounder (GAS), which is being developed by Saab Space AB and Omnisys AB, Sweden, and funded by European Space Agency (ESA). GAS utilizes interferometric synthetic aperture radiometry to obtain desired spatial (30 km) and temporal (nowcasting) resolution for measurement of atmospheric temperature and humidity profiles under all weather conditions. These parameters are decisively important to meteorological and climate models at all time scales.

Ground calibration of the calibration subsystems of MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) has been performed. The MIRAS instrument is the payload of European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission. The calibration subsystems are Calibration Subsystem (CAS), which is a noise distribution network, and Noise Injection Radiometer (NIR), which measures the noise levels of CAS and the average incident brightness temperature. This paper presents the used measurement approaches, related uncertainties, and the calibration results for the NIR and CAS. The results show that in spite of uncertainties, the characterization methods allow accurate ground calibration of the two subsystems. The performance of both subsystems meet the requirements.

In this paper, the thermally stabilized front-end of an L-band total power receiver is presented. Applications on the L-band have become one of the most important focus points in the field of passive microwave remote sensing. Measuring environmental parameters such as ocean salinity and soil moisture remain a challenge, posing strict requirements to instrument performance. For traditional radiometers, especially the stability of front-end components is critical. The principle of the stabilization technique and the actual design are described. The design also features an on-board active cold load for calibration purposes. The presented technique aims to stabilize the front-end section PCB with heaters to sub 0.05 C level.

Microwave radiometer measurements are viable technique for measurement of soil moisture and ocean salinity. Therefore, there is a strong interest in new L-band remote sensing radiometers. For example, European Space Agency's SMOS (soil moisture and ocean salinity) satellite is currently under development. SMOS applies Noise Injection Radiometer subsystem for the measurement of the average brightness temperature of the scene. This paper proposes the use of reference channel control method for the future L-band radiometer missions, e.g., for potential SMOS follow-on mission. According to analysis, significant improvement in radiometric resolution (up to 40%) would be achieved compared to traditional antenna channel noise injection architecture. The proposed system is based on the use of an active cold load (ACL) as cold reference.

The Helsinki University of Technology's airborne fully polarimetric profiling radiometer at 36.5 GHz has been used for wind-vector measurements over the Gulf of Finland. The results, collected in a series of measurements over a period of two years, are presented in this paper. The Fourier coefficients of the harmonics of the first three modified Stokes parameters (in brightness temperature) have been solved, and their behavior as a function of the measurement incidence angle and the wind speed has been examined, resulting in a linear model in the measurement range. In this paper, we show a clear relationship between the incidence angle and the third modified Stokes parameter (in brightness temperature), which has been used to compensate for aircraft motion during measurements. Furthermore, the sensitivity of the wind-speed measurement to the incidence angle has been studied, and a model for wind-speed retrieval as a function of the harmonic coefficients and incidence angle was developed.

Three flight models (FMs) of the reference radiometer of the Soil Moisture and Ocean Salinity (SMOS) mission have been developed and tested. SMOS is a joint mission of the European Space Agency, Centre National d'Etudes Spatiales of France, and Centre for the Development of Industrial Technology of Spain. The reference radiometer is a noise injection radiometer (NIR); the NIR subsystem FM has been developed by Elektrobit Microwave, Ltd., in collaboration with the Laboratory of Space Technology of Helsinki University of Technology, which has acted as a subcontractor. The NIR subsystem will be integrated into the microwave imaging radiometer using aperture synthesis (MIRAS) payload in 2006. MIRAS will be the sole instrument onboard the SMOS satellite. MIRAS has 66 total power receiver units (light and cost-effective front end) and three NIR units. The purpose of the NIR subsystem is 1) to provide precise measurement of the average brightness temperature scene for absolute calibration of the MIRAS image map, 2) to measure the noise temperature level of the internal active calibration sources of MIRAS [referred to as the calibration subsystem (CAS)], and 3) to form interferometer baselines, so-called mixed baselines, with the regular receiver units. The performance of the NIR is a decisive factor of overall MIRAS performance. In this paper, we present the design solutions for the NIR FMs, which enable the achievement of the mission goals set for the NIR subsystem. The results of the NIR test campaign, proving that the performance and environmental requirements are fulfilled, are also presented, and the outcome of the ground calibration campaign is analyzed. Furthermore, the orbital calibration scheme is depicted; the calibration scheme enables the NIR to measure its targets with precision.

Interferometric radiometry is a novel concept in remote sensing that is also presenting particular challenges for calibration methods. In this paper, we describe the calibration subsystem (CAS) developed for the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) interferometer of the Soil Moisture and Ocean Salinity (SMOS) satellite. CAS is important for the overall performance of the payload as it calibrates out the differences between the multiple receivers of MIRAS. SMOS is in the final phase of development and is due to launch in 2008.

In this paper, the authors introduce a method for calibrating the end-to-end phase imbalance of polarimetric radiometers using a digital correlation technique. The method is based on the measurement of linearly polarized field at -45deg and +45deg angles with respect to the polarization plane of the transmitted field. This way, the effects of the polarization purity of the transmitted field and the polarization separation and cross-coupling of the antenna of the radiometer can be cancelled out. The remaining uncertainty consists of the pointing accuracy of the radiometer with respect to the transmitted field. It has been shown here that this effect can be reduced to the level that will make the method feasible

A prototype reference radiometer for the Microwave Imaging Radiometer Using Aperture Synthesis (MIRAS) instrument of the Soil Moisture and Ocean Salinity satellite has been developed. The reference radiometer is an L-band fully polarimetric noise injection radiometer (NIR). The main purposes of the NIR are: 1) to provide precise measurement of the average fully polarimetric brightness temperature scene for absolute calibration of the MIRAS image map and 2) to measure the noise temperature level of the noise distribution network of the MIRAS for individual receiver calibration. The performance of the NIR is a decisive factor of the MIRAS performance. In this paper we present the operation principles and calibration procedures of the NIR, a measurement technique called blind correlation making measurements of full Stokes vector possible with the noise injection method, and finally experimental results verifying certain aspects of the design.

End-to-end calibration of the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) radiometer refers to processing the measured raw data up to dual-polarization brightness temperature maps over the earth's surface, which is the level 1 product of the Soil Moisture and Ocean Salinity (SMOS) mission. The process starts with a self-correction of comparators offset and quadrature error and is followed by the calibration procedure itself. This one is based on periodically injecting correlated and uncorrelated noise to all receivers in order to measure their relevant parameters, which are then used to correct the raw data. This can deal with most of the errors associated with the receivers but does not correct for antenna errors, which must be included in the image reconstruction algorithm. Relative S-parameters of the noise injection network and of the input switch are needed as additional data, whereas the whole process is independent of the exact value of the noise source power and of the distribution network physical temperature. On the other hand, the approach relies on having at least one very well-calibrated reference receiver, which is implemented as a noise injection radiometer. The result is the calibrated visibility function, which is inverted by the image reconstruction algorithm to get the brightness temperature as a function of the director cosines at the antenna reference plane. The final step is a coordinate rotation to obtain the horizontal and vertical brightness temperature maps over the earth. The procedures presented are validated using a complete SMOS simulator previously developed by the authors.

A complete and systematic noise analysis of radiometer front-ends, including both total power and correlation measurements, is presented. The procedure uses the concepts of noise waves and S-parameters, widely used in microwave systems design and takes into account full noise characterization of receivers including mismatch effects. The general formulation is compatible with known total power radiometer analysis and is specially appropriate in correlation radiometers for which the effect of nonideal components, such as input isolators, is analyzed. Along with numerical simulations, simple formulas are given to compute the measured visibility in nonideal conditions. The analysis is validated using experimental results consisting of correlation measurements of four receivers placed inside an anechoic chamber. Good agreement between theoretical predictions and experimental data is observed.

As the purpose of the noise injection radiometer (NIR) is to work as a reference for the MIRAS (Microwave Imaging Radiometer by Aperture Synthesis) measurements its performance and, especially, its stability is critical. The noise injection method, known from its inherent stability, was selected in order to meet the challenging requirement. An additional challenge is the fact that in orbit the NIR has only one really well known external target, namely the deep space. With the noise injection method one-point calibration is in principle possible, since the noise injection level can be determined with one known target. A series of measurements over relevant temperature ranges were carried out with the prototype in order to establish the temperature dependence, which is a critical factor of the overall stability. The results show that after the losses of the antenna and its connecting network are accounted for, due to the one-point calibration, an additional correction algorithm is needed to compensate for the thermal variations. Various correction algorithms were utilized and evaluated. It was shown that by using a well-designed correction algorithm the required performance is indeed achievable

An efficient algorithm for electromagnetic wave scattering from rough dielectric surfaces is being developed for the simulation of bistatic scattering and emission from ocean surface. The algorithm is bused on a single magnetic field integral equation (SMFIE) and the surface is discretized using Rao-Wilton-Glisson (RWG) triangular basis function. The new feature of the algorithm is the application of the adaptive integral method (AIM) with SMFIE for speeding up the calculation. The new method will enable accurate simulations over large ocean surfaces, which have been until recently prohibited by the lack of computer power and method efficiency. Although the solver is applicable to a wide range of practical problems, the main goal of this work is to simulate the bistatic scattering and emission from a large area, with respect to wavelength, of ocean surface, size of which is made possible by the efficiency of the new method. The surface is illuminated with a Gaussian beam so that the edges of the surface do not contribute to the results significantly. In this paper we present a solution to the rough surface scattering using SMFIE with AIM, so that the computation can he speeded up with fast Fourier transform (FFT)

A complete systematic analysis of correlation radiometer front-ends is presented. It takes into account full noise characterisation of the individual receivers and mismatch effects between different subsystems. The results are applied to the case where the front-ends include input isolator both ideal and imperfect. Along with numerical simulations, simple formulas are given to compute the visibility in these conditions, which is here defined as system visibility. The analysis is validated using experimental results consisting of correlation measurements of four receivers placed inside an anechoic chamber. Good agreement between theoretical predictions and experimental data is observed

An L-band noise injection radiometer (NIR) has been designed and implemented by Helsinki University ofTech- nology Laboratory ofSpace Technology f or the SMOS (Soil Moisture and Ocean Salinity) mission ofESA (1). The work is performed as a part of ESA's MIRAS Demonstrator Pilot Project-2 (MDPP-2) under a subcontract for EADS-CASA. Other partners in the MDPP-2 NIR project are Toikka Engineering Ltd. and Ylinen Electronics Ltd. NIR will work as a part ofthe MIRAS (Microwave Imaging Radiometer Using Aperture Synthesis) instrument. Its main purpose is (1) to provide precise measurement ofthe average brightness temperature scene for absolute calibration of the MIRAS image map and (2) to measure the noise temperature level ofthe internal active calibration source f or individual receiver calibration. The performance of NIR is a decisive factor ofthe MIRAS perf ormance. The challenge in the implemented, so-called blind correlation, method is the fact that there is additional noise in the correlated signal due to using the noise injection method. The main objective ofthis paper is to demonstrate the f ofthis technique. I. INTRODUCTION The precision of a noise injection radiometer is based on comparing the measured signal to two reference sources, the noise temperatures of which are known. This will remove the effect of the receiver gain and offset variations. The length of the noise pulse is then proportional to the antenna temperature (2). NIR will also be used in the MIRAS array as a regular receiver unit for interferometric image creation. In addition to measuring the horizontally and vertically polarized antenna noise temperature and the calibration net- work noise temperature, the MDPP-2 NIR was designed to provide fully polarimetric measurement capability. The Stokes parameters are retrieved using the same correlator, which the MIRAS uses for solving the correlation for the interferometric image creation. The so-called modified Stokes parameters are defined under the Rayleigh-Jeans approximation as (3)