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.

A spaceborne P-band signals of opportunity synthetic aperture radar concept is proposed for the remote sensing of terrestrial snow. We have completed a performance analysis assuming a formation flight of 3 to 5 SmallSats on one orbit plane. The spacing between the SmallSats is chosen so that their ground tracks will be separated by 50 to 100 m to allow the use of interferometric synthetic aperture radar processing technique to obtain a spatial resolution of a few hundred meters. A point system design has been completed to determine the antenna concept and to indicate the dependence of spatial resolution and signal to noise ratio on the number of receivers. The performance for range delay determination was analyzed to assess the impact of various error sources, including instrument receiver noise and ionospheric delay. The dominant error source is the ionospheric delay, which will be corrected using the split-spectrum algorithm. Our overall error budget analysis indicates that an accuracy of about 3 cm for the snow water equivalent in dry snow and 5 cm for the snow depth of wet snow can be achieved.

Microwave vegetation optical depth (VOD) and soil moisture (SM) can be simultaneously retrieved based on L-band radiometry with polarization information. VOD is indicative of the vegetation water content (VWC) because it captures the extinction of land surface emission. If the connectivity of VOD to VWC is robust, the pair of VWC-SM observations can be viable bases for understanding soil-plant-atmosphere water relations, providing new perspectives on ecosystem science. Simultaneous SM-VOD retrievals are feasible by inverting the τ–ω model with two independent datasets in dual channel algorithms. However, given correlated satellite vertical and horizontal brightness temperatures (TBvand TBh), an ill-posed inverse problem arises where TB errors result in high uncertainties of retrievals. In this study, we apply the Degrees-of-Information (DoI) metric and propose a Signal-to-Noise Ratio (SNR) metric to assess the “retrievability” of VOD given the SMAP TBv-TBhlinear dependence. The application of these metrics allows determining where the VOD retrievals are robust and reliable. This is a necessary step in supporting applications of VOD in ecology and hydrology. Results show that regions with mainly non-woody vegetation have the best potential for VOD retrievals, though regularization is necessary. We then assess VOD time variations from two regularization products that reduce the impact of under-determined inversions: the L3-DCA and the MTDCA, which constrain VOD time dynamics with and without using a priori VOD climatology, respectively. Though they both reduce noise, especially in the VOD retrievals, they result in differences in VOD seasonal amplitude and coupling to SM at high frequencies as we outline here.

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.

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

The 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 Soil Moisture Active Passive (SMAP) mission was designed to acquire and combine L-band radar and radiometer measurements for the estimation of soil moisture with 4% volumetric accuracy away from coastal zones. In regions near the coast or near inland bodies of water, the SMAP footprint contains land and water, resulting in errors in the soil moisture estimation. In this paper, we address the effort to extract the brightness temperature related to the land fraction or water fraction (depending on the center of the footprint location) from the affected SMAP measurements. We evaluate the performance of our algorithm over simulated data. We then show results over real data. The new SMAP upgraded product is expected to be delivered on April 2018.

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

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.

In this paper we summarize the effort to enhance the SMAP radiometer data. The applied technique is based on the Backus-Gilbert theory which is the classical estimation method in microwave radiometry. We show details of our implementation and summarize the assessment of the SMAP L1C_TB_E product.

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

In this paper we summarize the effort to enhance the resolution of SMAP radiometer data. The SMAP radiometer sampling of the Earth surface provides overlapping measurements along scan and along track. The oversampling combined with the given antenna gain function allows reconstruction of the scene with improved resolution. The applied technique is based on the Backus-Gilbert optimum interpolation theory, which is the classical inversion method in microwave radiometry. The results shown in this paper are based on the simulated SMAP measurements and are applicable to the real SMAP radiometer measurements.

The Soil Moisture Active Passive (SMAP) mission launched on Jan 31, 2015. The mission employs L-band radar and radiometer measurements to estimate soil moisture with 4\% volumetric accuracy at a resolution of 10 km, and freeze-thaw state at a resolution of 1-3 km [1]. Immediately following launch, there was a three month instrument checkout period, followed by six months of level 1 (L1) calibration and validation. A beta release of L1 radar data was available on July 1, 2015 and a validated release was made available starting on November 1, 2015. Work continued on L1 radar calibration and validation for several more months to improve the quality of the final product due to be released with the L2 validated release. In this presentation, we will discuss the radar processing algorithms and the calibration and validation activities for the L1 radar data.

Aquarius is a combined passive/active L-band microwave instrument developed to map the ocean surface salinity field from space [1]. The primary science objective of this mission is to monitor the seasonal and interannual variation of the large scale features of the surface salinity field in the open ocean with a spatial resolution of 150 km and a retrieval accuracy of 0.2 psu globally on a monthly basis. The measurement principle is based on the response of the L-band (1.413 GHz) sea surface brightness temperatures to sea surface salinity.

The L-band passive and active microwave geophysical model functions (GMFs) of ocean surface winds from the Aquarius data are derived. The matchups of Aquarius data with the Special Sensor Microwave Imager (SSM/I) and National Centers for Environmental Prediction (NCEP) winds were performed and were binned as a function of wind speed and direction. The radar HH GMF is in good agreement with the PALSAR GMF. For wind speeds above 10 m·s-1, the L-band ocean backscatter shows positive upwind-crosswind (UC) asymmetry; however, the UC asymmetry becomes negative between about 3 and 8 m·s-1. The negative UC (NUC) asymmetry has not been observed in higher frequency (above C-band) GMFs for ASCAT or QuikSCAT. Unexpectedly, the NUC symmetry also appears in the L-band radiometer data. We find direction dependence in the Aquarius TBV, TBH, and third Stokes data with peak-to-peak modulations increasing from about a few tenths to 2 K in the range of 10-25- m·s-1wind speed. The validity of the GMFs is tested through application to wind and salinity retrieval from Aquarius data using the combined active-passive algorithm. Error assessment using the triple collocation analyses of SSM/I, NCEP, and Aquarius winds indicates that the retrieved Aquarius wind speed accuracy is excellent, with a random error of about 0.75 m·s-1. The wind direction retrievals also appear reasonable and accurate above 10 m·s-1. The results of the error analysis indicate that the uncertainty of the GMFs for the wind speed correction of vertically polarized brightness temperatures is about 0.14 K for wind speed up to 10 m·s-1.

This paper describes an algorithm to simultaneously retrieve ocean surface salinity and wind from combined passive/active L-band microwave observations of sea surfaces. The algorithm takes advantage of the differing response of brightness temperatures and radar backscatter to salinity, wind speed, and direction. The algorithm minimizes the least square error (LSE) measure, signifying the difference between measurements and model functions of brightness temperatures and radar backscatter. Three LSE measures with different measurement combinations are tested. One of the LSE measures uses passive microwave data only with retrieval errors reaching 2 psu for salinity and 2 m/s for wind speed. The second LSE measure uses both passive and active microwave data for vertical and horizontal polarizations. The addition of active microwave data significantly improves the retrieval accuracy by about a factor of five. To mitigate the impact of Faraday rotation on satellite observations, we propose the third LSE measure using measurement combinations invariant under the Faraday rotation. For Aquarius, the expected root-mean-square SSS error will be less than 0.2 psu for low winds and increases to 0.3 psu at 25-m/s wind speed for warm waters, and the accuracy of retrieved wind speed will be high (about 1-2 m/s or lower). Our results suggest that combining passive and active microwave observations will allow retrieval of sea surface salinity along with the wind speed and direction. In particular, the LSE measure invariant under the Faraday rotation will be directly applicable to spaceborne missions, such as the NASA Aquarius and Soil Moisture Active Passive missions.

Over the last few years, researchers have determined that sea spray from breaking waves can have a large effect on the magnitude and distribution of the air-sea energy flux at hurricane-force wind speeds. Characterizing the fluxes requires estimates of the height-dependent droplet size distribution (DSD). Currently, the few available measurements have been acquired with spectrometer probes, which can provide only flight-level measurements. As such, in-situ measurement of near-surface droplet fluxes in hurricanes with these instruments is, at best, extremely challenging, if at all possible. This paper describes an airborne dual-wavelength radar profiler concept to retrieve the DSD of sea spray.

This paper discusses an effort to create a fast, yet accurate GNSS-R simulator capable of spacecraft-altitude studies. A number of new techniques will be presented which were developed to significantly increase the simulation speed. The end goal of this work is to assess measurement fundamentals such as ocean altimetry accuracy as a function of altitude and antenna size. The first application of this new simulator was to evaluate the GPS L2-L5 widelane signal for altimetric applications. It has been suggested that this widelane signal could result in an accurate, coherent phase-delay height observable. Simulation results, along with a simple physical model, indicate this widelane combination does not produce a coherent phase observable under typical conditions. The incoherence appears to be due to the surface area corresponding to the signal's autocorrelation function being much larger than the widelane Fresnel zone.