Soil Moisture Active Passive Observations Research Articles

Seasonal-scale intercomparison of SMAP and fused SMOS-SMAP soil moisture products

Two L-band passive microwave satellite sensors, onboard the Soil Moisture and Ocean Salinity (SMOS) launched in 2009 and Soil Moisture Active Passive (SMAP) launched in 2015, are specifically designed for surface soil moisture (SM) monitoring. The first global continuous fused L-band satellite SM product based on SMOS and SMAP observations (SMOS-SMAP-INRAE-BORDEAUX, the so-called Fused-IB) was recently released to the public. Currently, the performance of Fused-IB has only been evaluated collectively over the entire data records in the study period, without specific evaluation for individual seasons. To fill this gap, this study intercompared the Fused-IB and the enhanced SMAP-L3 version 6 (SMAP-E) SM products against in situ SM data from the International Soil Moisture Network (ISMN) from 2016 to 2020 regarding the whole period and different seasons. We aim to investigate the performance of these two products at different time scales and to explore the potential eco-hydrological factors (i.e., precipitation and vegetation) driving their seasonal variations. Results show that both SM products are in good agreement with the in situ measurements, demonstrating high median correlation (R) and low ubRMSD (median R = 0.70 and ubRMSD = 0.058 m3/m3 for Fused-IB vs. R = 0.68 and ubRMSD = 0.059 m3/m3 for SMAP-E) during 2016–2020. For most land use and land cover (LULC) types, Fused-IB outperformed SMAP-E with higher accuracy and lower errors, particularly in forests, partly due to the advantage of the robust SMAP-IB (SMAP-INRAE-BORDEAUX) algorithm used to generate Fused-IB in forests, which avoids the pronounced saturation effects of vegetation optical depth caused by relying on optical information. Besides, both products had superior performances across most LULC types in summer (JJA) and autumn (SON), yet increased uncertainties were observed in forests, grasslands, and croplands during spring (MAM) and winter (DJF). These uncertainties could be mainly attributed to the effects of vegetation growth in forests, grasslands and croplands, and the interception of water from rainfall events in grasslands. The results of this study can serve as a reference for algorithm developers to enhance the accuracy of SM and thus promote hydro-meteorological applications that benefit from L-band radiometer soil moisture products.

Retrieving forest soil moisture from SMAP observations considering a microwave polarization difference index (MPDI) to τ-ω model

Estimating soil moisture from microwave brightness temperature is extremely challenging in densely vegetated areas. The soil moisture retrieved from the Soil Moisture Active Passive (SMAP) measurements tends to be consistently overestimated, sometimes exceeding the saturation level of mineral soils. Therefore, the retrieved soil moisture cannot detect or monitor climate extremes, such as floods and droughts for forests, natural resource management, and climate change research. We hypothesize that the main issue is that the scattering albedo (ω) and the optical depth (τ) are parameterized solely with NDVI (Normalized Difference Vegetation Index), neglecting the polarization characteristics from vegetation structure. This study proposes a weighting factor between scattering and optical thickness, a function of MPDI (Microwave Polarization Difference Index), and applies it to both parameters simultaneously to increase the scattering effect and decrease the attenuation effect in high MPDI. The validation results based on the Climate Reference Network revealed that considering MPDI is critical in reducing soil moisture overestimation errors and obtaining more accurate soil moisture over forested regions. This results in correlation improving from 0.36 to 0.44, a decrease in ubRMSE from 0.179 to 0.125 cm³cm−³, and bias lowering from 0.127 to 0.060 cm³cm−³ in comparison with the SMAP measurements over forested regions.

High temporal resolution quasi-global landscape soil freeze–thaw map from spaceborne GNSS-R technology and SMAP radiometer measurements

Landscape freeze–thaw (F/T) state parameters are an integral part of research on terrestrial hydrological processes, vegetation growth dynamics, and terrestrial–atmospheric trace gas exchange. Therefore, the development of large-scale, continuous, and rapid F/T observation records is essential. This study proposes a scheme to effectively retrieve quasi-global daily soil F/T states from spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) observations. This method takes into account the GNSS-R surface reflectivity and its influence factor. Specifically, the method employed surface reflectivity derived from Cyclone Global Navigation Satellite System (CYGNSS) data from 2018 to 2020, along with vegetation opacity data from Soil Moisture Active Passive (SMAP) as input layers in random forests for F/T retrieval. This approach provides F/T results on a quasi-global scale, does not rely on continuous F/T observations, and employs the least ancillary data. To validate the retrieval results, SMAP F/T data over the same period and temperature data from more than 100 ground stations were also used as reference. Results showed that the F/T retrieval accuracy of CYGNSS was 95.5% and 82.0% with respect to the SMAP data and data from independent in situ stations, respectively. Given the high temporal coverage of the CYGNSS F/T results, the temporal coverage improvement is 23.3% compared with the actual SMAP observations on a daily scale. Furthermore, as gap-filling data complemented SMAP, CYGNSS data can effectively improve the SMAP F/T sample interval (accuracy improvement of 26.0%). More importantly, the F/T accuracy of the CYGNSS and SMAP combination based on monthly performance is 85.1%, i.e., 20.5% better than that of the existing SMAP daily F/T product. Overall, the quasi-global daily F/T results obtained from CYGNSS and SMAP measurements can complement existing databases with well-defined accuracy.

Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting

AbstractTemporal gaps in satellite‐based soil moisture (SM) products are a persistent issue. This study presents an entirely observation‐based method to derive volumetric SM content for filling gaps in Soil Moisture Active Passive (SMAP) retrievals. Using a water balance equation, 12‐hr topsoil water amount variations are determined based on observed precipitation from the Global Precipitation Measurement Mission (inflow) and a hydrologic loss function (outflow) built on SMAP dry‐downs. A temporally seamless SM product, composed of SMAP dry‐downs and precipitation‐driven moisture approximations, was generated as a secondary outcome in determining optimal water balance parameters. This data set maintains the original SMAP SM dynamics with a median Pearson correlation (R) of 0.69 and an unbiased root‐mean‐square error (ubRMSE) of 0.05 m3/m3. Using these parameters and available SMAP observations, a 12‐hourly SM product was produced over the conterminous United States. Validated against in situ measurements, this 12‐hourly SM product exhibits good performance with a median R of 0.63 and captures most SM peaks induced by heavy rainfall. A time series examination revealed the produced 12‐hourly SM product closely corresponds to in situ SM variations and outperforms two other SMAP‐based 12‐hourly SM products gap‐filled using temporal linear interpolation and a three‐dimensional smoothing approach, especially during sparse SMAP data periods. The proposed scheme's validity is further verified by the comparable performance of the exclusive filled‐on SM estimates. Utilizing the 12‐hourly SM data set and its paired hydrologic losses could enhance the quantification connections among the hydrologic components and benefit the understanding of land‐surface hydrology.

Impact of SAR-based vegetation attributes on the SMAP high-resolution soil moisture product

NASA's Soil Moisture Active Passive (SMAP) mission employs L-band radiometer observations with a spatial resolution of ~33 km to produce global soil moisture products at various spatial resolutions and regular intervals. Among these products is the SMAP-Sentinel active-passive high-resolution soil moisture, available at spatial resolutions of 1 km and 3 km. The SMAP-Sentinel product combines data from the SMAP L-band radiometer and the Copernicus Sentinel-1A and Sentinel-1B C-band Synthetic Aperture Radar (SAR) observations. The SMAP-Sentinel high-resolution soil moisture product enables various hydrological and agricultural applications that were previously beyond the capability of coarser resolution radiometer-only products. However, the assessment of SMAP-Sentinel product over SMAP core validation sites (CVS) has revealed high errors and biases, particularly in agricultural regions. We suspect that the discrepancy may be related to the vegetation attribute parameters (e.g., vegetation optical depth, τ) used in the SMAP-Sentinel baseline Single-Channel Algorithm (SCA) microwave emission model (i.e., τ-ω model) for soil moisture retrievals, which might be out of sync due to reliance on NDVI climatology in the τ estimation. In this study, we leveraged and evaluated the advantage of using vegetation attribute information inherently available through the SMAP ratiometer and Sentinel- 1A/1B SAR backscatter as an alternative to NDVI climatology-derived τ in the SCA to enhance soil moisture retrievals. The SAR cross-polarization (vh) backscatter potentially contains vegetation attribute information that can be correlated with the coarse resolution τ derived from SMAP observations. Therefore, we hypothesized that including Sentinel- 1A/1B vh observations to derive high-resolution (1 km and 3 km) τ, and subsequently incorporating it into the SCA, can significantly improve the accuracy of the SMAP-Sentinel soil moisture product. We established a statistical relationship between Sentinel- 1A/1B SAR backscatter and the SMAP Dual-Channel Algorithm (DCA)-based τ retrieval at ~33 km, for a specific region and different landcover classes at a global extent, using seven years of observations (2015–2022). Further, we developed an algorithm to downscale the SMAP DCA τ of ~33 km to 1 km and 3 km resolutions based on the developed statistical relationships, enabling us to capture the actual temporal variability in vegetation attributes (i.e., τ) at higher resolutions. The downscaled τ values were then incorporated into the SCA to retrieve SMAP-Sentinel high-resolution (1 km and 3 km) soil moisture. The validation analysis of high-resolution soil moisture retrievals demonstrates that the inclusion of the downscaled τ approach significantly improved the performance of the SMAP-Sentinel product by reducing both bias and error standard deviation compared to the retrievals based on τ derived from the NDVI climatology.

Simulation of SMAP and AMSR2 observations and estimation of multi-frequency vegetation optical depth using a discrete scattering model in the Tibetan grassland

Passive microwave observation at multiple frequencies has received increasing research interests due to its capability to provide comprehensive information of land surface properties. This paper contributes to the simulation of land surface emission and estimation of vegetation optical depth (VOD) at multiple frequencies using a discrete scattering model with a single set of model parameter values. Validity of the Tor Vergata (TVG) discrete scattering model in simultaneously reproducing the Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) and Advanced Microwave Scanning Radiometer 2 (AMSR2) C- (6.925 GHz) and X-band (10.7 GHz) observations over the Tibetan grassland ecosystem is evaluated. Frequency-specific and multi-frequency calibration strategies are implemented to find the suitable set of model parameter values and to isolate the impact of frequency on parameter values. On this basis, the calibrated TVG model is further used to estimate the VOD, and to investigate the impact of microwave frequency and observation angle on the emission simulations and VOD parameterization.The results show that both frequency-specific and multi-frequency calibration strategies achieve comparable and reasonable simulations of SMAP and AMSR2 observations, confirming the feasibility of using an identical physically-based model (i.e. the calibrated TVG model) to simulate multi-frequency land emission driven by a single set of model parameter values. As such, the dependence of emission components and VOD on frequency can be elaborated after isolating the impact of frequency on parameter values. The VOD values derived from the TVG simulations generally increase with increasing frequency and can be linearly correlated to the LAI variations, while current satellite-based retrievals have almost the same magnitude at the L-, C-, and X-band. The explanation for this can be that the retrieved VOD is different from the theoretical definition. Sensitivity test performed using the calibrated TVG model further shows that polarization-dependence of VOD becomes more apparent with the increasing observation angle and frequency. New parameterization has thus been developed to characterize the dependence of VOD on the frequency, observation angle, and polarization for grassland based on the results of sensitivity test. This study may provide new insights in improving model of land emission and retrievals of SM and VOD with physical interpretability based on multi-frequency satellite observations.

From field observations to temporally dynamic soil surface roughness retrievals in the U.S. Corn Belt

Soil surface roughness in the U.S. Corn Belt varies outside of the growing season due to rainfall and soil management activities such as tillage. This is not consistent with the assumption that roughness is a static parameter in the Soil Moisture Active Passive (SMAP) soil moisture retrieval algorithm. SMAPVEX16-IA, an extensive field campaign focused on calibration of SMAP retrievals in croplands, occurred during a period when roughness is theoretically at a minima. Satellite-scale measurements of rms height and correlation length collected via pinboard, gridboard, and lidar methodologies resulted in a ensemble of model roughness coefficients that, at h = [0.13, 0.97], is significantly rougher than the original SMAP assumption in croplands of h = 0.108. Comparing simulated brightness temperatures using the ensemble h to SMAP observations resulted in a best-fit angular sensitivity coefficient of N=−2 for both horizontal and vertical polarizations. Satellite-scale h is then retrieved for a four-year study period (excluding growing seasons) via the single channel algorithm from SMAP brightness temperatures and in situ observations of soil moisture and temperature. Roughness retrievals are highly variable in the spring as rainfall events decrease h followed by an increase during soil dry-down; h steadily increases in the fall post-harvest as fields are tilled across the network. Retrievals of h are effectively independent of polarization and demonstrate the potential to simultaneously retrieve soil moisture and roughness in the absence of crops. These soil roughness retrievals could provide new information on tillage and other soil management activities.

Impact of Rain Effects on L-Band Passive Microwave Satellite Observations Over the Ocean

L-band passive microwave remote sensing of ocean surfaces is hampered by uncertainties due to the contribution of precipitation. However, modeling and correcting rain effect are complicated because all the precipitation contributions from the atmosphere and sea surface, including the rain effect in the atmosphere, water refreshing, rain-perturbed sea surface, and rain-induced local wind, are coupled. These rain effects significantly alter L-band microwave satellite measurements. This study investigates the impact of precipitation on the satellite measured brightness temperature (TB) and proposes a correction method. The results show that the rain-induced TB increase is approximately 0.5–1.4 K in the atmosphere, depending on the incidence angle and polarization. Moreover, the rain effect on sea surface emissions is more significant than that in the atmosphere. The result shows that rain effects on sea surface emissions are higher than 3 K for both polarizations when the rain rate is higher than 20 mm/h. We validate the rain effect correction model based on Soil Moisture Active Passive (SMAP) observations. The results show that the TBs at the top of the atmosphere (TOA) simulated by the model are in a good agreement with the SMAP observations, with root-mean-square errors (RMSEs) of 1.137 and 1.519 K for the horizontal and vertical polarizations, respectively, indicating a relatively high accuracy of the established model. Then, a correction model is applied to sea surface salinity (SSS) retrieval for analysis, and the results show that the developed model corrects the underestimation in SSS retrieval. Finally, the rain effect correction model is validated with Aquarius observations in three regions, and it is found that the RMSEs of the corrected TOA TBs range from 1.013 to 1.608 K, which is higher than those without rain effect correction (RMSEs range from 2.078 to 3.894 K). Overall, the model developed in this study provides relatively a good accuracy for rain effect correction.

Reconstruct SMAP brightness temperature scanning gaps over Qinghai-Tibet Plateau

• SMAP brightness temperature ( TB ) scanning gaps were compensated. • Daily variation of TB was analyzed in function of influencing factors. • Seamless TB benefits the refinements of SMAP soil moisture products. Soil Moisture Active Passive (SMAP) satellite was used to monitor global soil moisture and freeze–thaw state using surface brightness temperature (TB). However, due to the limitation of scanning width and shifting track, the daily TB observation had stripe gaps in varying degrees, which imped scientific applications of SMAP official product. To solve this issue, we proposed a temporal fitting algorithm to produce daily seamless TB data over Qinghai-Tibet Plateau (QTP) from 2015 to 2021. This method was composed of two steps: (1) overall time trend fitting which used a spline function to fit the overall mean time trend for each pixel from 2015 to 2021; (2) daily variation correction which accounted for several influential factors including surface temperature, precipitation, and vegetation to establish an Ordinary Least Squares (OLS) model for predicting daily variation of TB in numerous time slices. Consequently, the missing TB observation was reconstructed by combining the overall temporal trend and daily variation. A 10-fold cross-validation and product comparison were carried out to validate the robustness of the proposed method. For cross-validation, we randomly removed available SMAP observations in certain periods as unavailable, and then compared the reconstructed values with the removed practical ones. The cross-validation results indicated the reconstructed TB agreed well with actual SMAP TB, with a high overall coefficient of determination (R 2 = 0.94) and low RMSE (RMSE = 3.7 K), implying the desirable performance of our proposed method. By comparison with Soil Moisture and Ocean Salinity (SMOS) TB, the reconstructed data had an approximate performance with actual SMAP TB. Furthermore, the effects of surface temperature and precipitation on TB in different periods (frozen and unfrozen) over QTP were also analyzed. Our method has the potential to generate seamless SMAP products.

A New Method for Generating the SMOPS Blended Satellite Soil Moisture Data Product without Relying on a Model Climatology

Soil moisture operational product system (SMOPS) is developed by National Oceanic and Atmospheric Administration (NOAA) to provide the real-time blended soil moisture (SM) for numeric weather prediction and national water model applications. However, all individual satellite SM data ingested into the current operational SMOPS are scaled to global land data assimilation system (GLDAS) 0–10 cm SM climatology before the combination. As a result, the useful information from the original microwave SM retrievals could be lost, and the GLDAS model errors could be brought into the final SMOPS blended product. In this paper, we propose to scale the individual SM retrievals to the soil moisture active passive (SMAP) data through building regression models. The rescaled individual SM data and the SMAP observations then have similar climatology and dynamics, which allows producing the SMOPScdr (distinguishing with the current operational SMOPSopr) data using an equal-weight averaging approach. With respect to the in situ SM measurements, the developed SMOPScdr is more successful tracking the surface SM status than the individual satellite SM products with significantly decreased errors. The proposed method also preserves the climatology of the reference SMAP data for the period when SMAP is not available, allowing us to produce a long-term SMOPScdr data product.

Evaluating the Variability of Surface Soil Moisture Simulated Within CMIP5 Using SMAP Data

AbstractThere are significant biases and uncertainties in the simulated soil moisture with land surface models. Here we evaluate multimodel differences in Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products on different time scales. The variability of surface soil moisture (SSM) within three frequency bands (7–30 days, 30–90 days, and 90–365 days) after normalization is quantified using Fourier transform for the evaluation. Compared to the SMAP observations, the simulated SSM variability within CMIP5 is underestimated in the two higher frequency bands (by 72% and 56%, respectively) and overestimated in the lowest frequency band (by 113%). In addition, these differences concentrate in regions with larger SSM. Finally, these multimodel differences are found to be significantly correlated with mean climate conditions rather than soil texture. This study identifies the spatiotemporal distribution of the model deficiencies within CMIP5 and finds they are systematic in the long‐term simulation on a global scale.

Assessing Performances of Multivariate Data Assimilation Algorithms with SMOS, SMAP, and GRACE Observations for Improved Soil Moisture and Groundwater Analyses

Multivariate data assimilation (DA) of satellite soil moisture (SM) and terrestrial water storage (TWS) observations has recently been used to improve SM and groundwater storage (GWS) simulations. Previous studies employed the ensemble Kalman approach in multivariate DA schemes, which assumes that model and observation errors have a Gaussian distribution. Despite the success of the Kalman approaches, SM and GWS estimates can be suboptimal when the Gaussian assumption is violated. Other DA approaches, such as particle smoother (PS), ensemble Gaussian particle smoother (EnGPS), and evolutionary smoother (EvS), do not rely on the Gaussian assumption and may be better suited to non-Gaussian error systems. The objective of this paper is to evaluate the performance of these four DA approaches (EnKS, PS, EnGPS, and EvS) in multivariate DA systems by assimilating satellite data from the Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery And Climate Experiment (GRACE) missions into the Community Atmosphere and Biosphere Land Exchange (CABLE) land surface model. The analyses are carried out in Australia’s Goulburn River catchment, where in situ SM and groundwater data are available to comprehensively validate the DA performance. Results show that all four DA approaches have outstanding performances and improve correlation coefficients of SM and GWS estimates by ~20% and 100%, respectively. The EvS outperforms the others, but its benefit is relatively marginal compared to Gaussian approaches (e.g., EnKS). This is due to the fact that SM and TWS error distributions in this study are close to Gaussian: a suitable condition for, e.g., EnKS, EnGPS. The robust performance of EvS appears to be the optimal approach for jointly assimilating multi-source hydrological observations to improve regional hydrological analyses.

Validation of Soil Moisture Data Products From the NASA SMAP Mission

The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error &lt;0.04 m³&#x002F;m³). The validation approach also includes other (&#x201C;sparse network&#x201D;) <i>in situ</i> SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products&#x2019; performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the <i>L</i>-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio&#x2013;temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

A Novel Approach to Map the Intensity of Surface Melting on the Antarctica Ice Sheet Using SMAP L-Band Microwave Radiometry

The polar ice sheets have undergone unprecedented melt events in recent years, which have consequences for the ice sheet mass balance and stability, and global sea level. In this article, we employed L-band (1.4 GHz) brightness temperature observations collected by NASA's Soil Moisture Active Passive (SMAP) mission to investigate the extent, duration, and intensity of melt events on the Antarctic Ice Sheet from 2015 to 2020. The observed microwave response depends on the sensor measurement frequency. Our hypothesis for this article is that the relatively long wavelength (21 cm) SMAP observations can detect a wider range of surface wetness conditions relative to shorter wavelength microwave observations that attain signal saturation at relatively lower wetness levels and within shallower surface layers. SMAP provides nearly all-weather surface monitoring over all of Antarctica twice daily with morning and evening overpasses at about 40 km spatial resolution. We applied an empirical threshold algorithm using horizontally and vertically polarized microwave brightness temperature differences to detect surface melt events over Antarctica. The results show that the SMAP empirical algorithm can be used to detect melt extent and duration, and the geophysical model-based algorithm can be used to detect variations in snow wetness, which serve as an indicator of melt intensity. Analysis of the melt seasons between 2015 and 2020 shows that even though the melt extent in 2019–2020 was not as large as during the 2015–2016 melt season, it was significantly more intense, particularity on the West Antarctic Ice Sheet.

Evaluation of Multiscale SMAP Soil Moisture Products in Forested Environments

Quantifying the availability of soil moisture in forested environments has many implications for their ecosystem, and therefore the ability to map its dynamics accurately becomes important. Remote sensing offers the only viable strategy for cost-effective, global measurements of soil moisture and the launch of satellites with L-band microwave sensors such as the Soil Moisture Active Passive (SMAP) has extended those capabilities to forested areas. A number of retrieval algorithms have been developed by the SMAP mission that have generated multi-scale data products at 9 km, 3 and 1 km with the two finer-scale datasets derived by combining SMAP and Sentinel-1 C-band observations. In this study, we evaluated the ability of the three SMAP products to estimate the magnitude and dynamics of soil moisture in a relatively challenging environment of dense forest cover and topographic complexity using ground measurements from seven sites in New Zealand. We found that the finer-scale products had an overall smaller error and higher correlations when compared with the ground measurements, but did exhibit noise especially as canopy closure increased. On the other hand, the 9-km product had larger errors but was still able to capture the temporal dynamics of soil moisture. Our results are consistent with recent studies evaluating SMAP in forested environments, and the improved performance of the finer-scale products suggests a pathway towards accurate estimates of forested soil moisture using multi-scale data fusion.

Tropical Cyclone Center and Symmetric Structure Estimating From SMAP Data

We propose a methodology to estimate the tropical cyclone (TC) center location associated with the additional TC parameters of the radius of maximum wind (RMW) and intensity, purely from ocean winds observed by the Soil Moisture Active Passive (SMAP) radiometer. This method assumes a symmetric vortex. To demonstrate the method, we analyze 28 SMAP wind fields collected during 11 TCs. Verification of the estimated hurricane centers and wind distributions along the radius is compared with measurements provided by the airborne Stepped Frequency Microwave Radiometer (SFMR) collected during its flying through hurricane cores and to sea surface wind fields derived from spaceborne synthetic aperture radar data. The significance of this simple method is that TC centers, intensities, and RMW can be estimated purely from SMAP wind products, despite SMAP’s low spatial resolution. Comparing these 28 model results to the aircraft measurement (f-deck) and the best track (BT) data, we show that for the latitudes and longitudes of the TC centers, the standard deviations are 0.28° and 0.29°, respectively, both with a correlation of 1.00. For the detected intensity, the standard deviation is 8.11 m/s, and the correlation is 0.84. We note that the TC intensity detected by this method can be even stronger than the maximum winds observed by the relatively low-resolution SMAP observations.

Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moisture

Abstract. The National Aeronautics and Space Administration (NASA) Soil Moisture Active-Passive (SMAP) mission characterizes global spatiotemporal patterns in surface soil moisture using dual L-band microwave retrievals of horizontal (TBh) and vertical (TBv) polarized microwave brightness temperatures through a modeled mechanistic relationship between vegetation opacity, surface scattering albedo, and soil effective temperature (Teff). Although this model has been validated against in situ soil moisture, there is a lack of systematic characterization of where and why SMAP estimates deviate from the in situ observations. Here, we assess how the information content of in situ soil moisture observations from the US Climate Reference Network contrasts with (1) the information contained within raw SMAP observations (i.e., “informational random uncertainty”) derived from TBh, TBv, and Teff themselves and with (2) the information contained in SMAP's dual-channel algorithm (DCA) soil moisture estimates (i.e., “informational model uncertainty”) derived from the model's inherent structure and parameterizations. The results show that, on average, 80 % of the information in the in situ soil moisture is unexplained by SMAP DCA soil moisture estimates. Loss of information in the DCA modeling process contributes 35 % of the unexplained information, while the remainder is induced by a lack of additional explanatory power within TBh, TBv, and Teff. Overall, retrieval quality of SMAP DCA soil moisture, denoted as the Pearson correlation coefficient between SMAP DCA soil moisture and in situ soil moisture, is negatively correlated with the informational uncertainties, with slight differences across different land covers. The informational model uncertainty (Pearson correlation of −0.59) was found to be more influential than the informational random uncertainty (Pearson correlation of −0.34), suggesting that the poor performance of SMAP DCA at some locations is driven by model parameterization and/or structure and not underlying satellite measurements of TBh and TBv. A decomposition of mutual information between TBh, TBv, and DCA soil moisture shows that on average 58 % of information provided by TBh and TBv to DCA estimates is redundant. The amount of information redundantly and synergistically provided by TBh and TBv was found to be closely related (Pearson correlations of 0.79 and −0.82, respectively) to the retrieval quality of SMAP DCA. TBh and TBv tend to contribute large redundant information to DCA estimates under surfaces or conditions where DCA makes better retrievals. This study provides a baseline approach that can also be applied to evaluate other remote sensing models and understand informational loss as satellite retrievals are translated to end-user products.

Classification of the frozen/thawed surface state of Northern land areas based on SMAP and GCOM-W1 brightness temperature observations at 1.4 GHz and 6.9 GHz

ABSTRACT In this letter, the method created earlier by the authors and the information product SPL3FTP_E of the Soil Moisture Active Passive (SMAP) satellite for determining frozen/thawed state of soil surface on the example of test sites placed on North Slope of Alaska, U.S.A., Canada, Finland and Russian Federation were compared. As an indicator of the frozen/thawed state of soil surface, the polarization index calculated on the basis of the reflectivity of soils was proposed. The soil reflectivity was determined in the L-band based on the SMAP radiometric observations at a frequency of 1.4 GHz using the values of brightness temperatures measured by the Global Change Observation Mission – Water 1/Advanced Microwave Scanning Radiometer 2 (GCOM-W1/AMSR2) at a vertical polarization and a frequency of 6.9 GHz, as an estimate of the soil effective temperature. As a result, it was shown that the developed method makes it possible to increase accuracy of the frozen/thawed states determination of soil surface from 3% to 9% in relation to the SMAP data (SPL3FTP_E) for twelve Arctic test sites.

Study on direction dependence of the fully polarimetric wind-induced ocean emissivity at L-band using a semi-theoretical approach for Aquarius and SMAP observations

The wind direction dependence of the fully polarimetric ocean emissivity at L-band is studied using a semi-theoretical approach. The model, which is tuned and validated against Aquarius and Soil Moisture Active-Passive (SMAP) mission observations, incorporates the two-scale polarimetric ocean emission model, an improved directional wave spectrum, the Anguelova and Gaiser, 2013 (AG13) foam emissivity model and a novel foam coverage model. Wind speeds up to 35 m/s are considered. Comparisons of model simulations to satellite data show that the overall accuracy of model could reach 0.2 K, which is slightly worse than the Aquarius geophysical mode function (GMF) error of about 0.14 K. The negative and positive upwind-crosswind asymmetries of L-band ocean emission and the transition of the phase signatures versus wind speed are well reproduced for all polarizations by the proposed model. The average standard deviations of the differences of polarimetric brightness temperature (TB) between observations and model are less than about 0.16 K for wind speeds up to 15 m/s. Simulations also indicate the anisotropy of L-band ocean emissivities for V- and H- pol would be saturated at high wind speeds. The directional signals at the third Stokes component begin to converge at around the wind speed of 24 m/s. The L-band TB variations due to wind direction for the third Stokes parameter show less sensitivity to wind speed at larger observation angles, while the direction-induced TB variations for the fourth Stokes are more sensitive to wind speed with the increase of observation angle.

Global Soil Water Estimates as Landslide Predictor: The Effectiveness of SMOS, SMAP, and GRACE Observations, Land Surface Simulations, and Data Assimilation

AbstractThis global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery and Climate Experiment (GRACE) remote sensing data; Catchment Land Surface Model (CLSM) simulations; and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300 km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011–16, unless the CLSM model-only soil water content is already high (≥50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015–19) more generally updates the soil water conditions toward higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.