SMAP Satellite Research Articles

Microwave Radiometry at Frequencies From 500 to 1400 MHz: An Emerging Technology for Earth Observations.

Microwave radiometry has provided valuable spaceborne observations of Earth’s geophysical properties for decades. The recent SMOS, Aquarius, and SMAP satellites have demonstrated the value of measurements at 1400 MHz for observing surface soil moisture, sea surface salinity, sea ice thickness, soil freeze/thaw state, and other geophysical variables. However, the information obtained is limited by penetration through the subsurface at 1400 MHz and by a reduced sensitivity to surface salinity in cold or wind-roughened waters. Recent airborne experiments have shown the potential of brightness temperature measurements from 500–1400 MHz to address these limitations by enabling sensing of soil moisture and sea ice thickness to greater depths, sensing of temperature deep within ice sheets, improved sensing of sea salinity in cold waters, and enhanced sensitivity to soil moisture under vegetation canopies. However, the absence of significant spectrum reserved for passive microwave measurements in the 500–1400 MHz band requires both an opportunistic sensing strategy and systems for reducing the impact of radio-frequency interference. Here, we summarize the potential advantages and applications of 500–1400 MHz microwave radiometry for Earth observation and review recent experiments and demonstrations of these concepts. We also describe the remaining questions and challenges to be addressed in advancing to future spaceborne operation of this technology along with recommendations for future research activities.

Snow wetness retrieved from close-range L-band radiometry in the western Greenland ablation zone

AbstractClose-range (CR) L-band radiometry and quasi-simultaneous in situ snow characterizations were conducted in May 2019 at the Swiss Camp research site in the ablation zone of the western Greenland ice sheet. Snow liquid-water and its melt/refreeze are retrieved from microwave antenna temperatures measured with the ground-based L-band radiometer ELBARA-III. The emission model (EM) used in the retrieval is a two-layer configuration of the ‘L-Band Specific Microwave Emission Model of Layered Snowpack’. Consistent snow wetness retrievals were achieved from both single- and multi-angle CR observations of L-band antenna temperatures. This suggests that multi-angle observation is not a pre-requisite for snow wetness retrieval. Therefore, in addition to soil moisture and ocean salinity (SMOS) multi-angle measurements, snow wetness can be estimated from spaceborne L-band brightness temperatures measured at a single observation angle, such as from NASA's SMAP satellite. Our results provide partial validation of a recently presented snow wetness retrieval approach based on the same EM and applied over Greenland using multi-angle SMOS brightness temperatures. Agreement between measured CR antenna temperatures and SMOS brightness temperatures is found to be within the 95% confidence intervals of ELBARA-III and SMOS measurement uncertainties. Our measurements confirm the modeled response of antenna temperatures to diurnal variations of snow wetness.

Detection of soil freeze/thaw states in the Arctic region based on combined SMAP and AMSR-2 radio brightness observations

ABSTRACT In this study, a new approach to identify the freeze/thaw states of tundra topsoil was developed based on the polarization ratio index, which was calculated from the reflectivity values of soil. Reflectivity was estimated from radiometric measurements of the SMAP satellite using the values of vertical polarization brightness temperature measured by the AMSR-2 radiometer at 6.9 GHz; this value was used to characterize the effective temperature of the soil. The proposed approach was tested using weather station data on soil surface temperatures for six test sites located in the North Slope of Alaska and the Yamal Peninsula collected from April 2015 to June 2018. The modified polarization ratio index, calculated from values of reflectivity rather than from brightness temperatures, significantly improved the possibility of determining the reference values of the index in the winter and in the summer. During testing, the modified index showed a good correlation between the dates of transition through the threshold level and soil temperature transition through 0ºC, as recorded at meteorological stations.

Predictability of Naïve Bayes classifier for lahar hazard mapping by weather radar

The aim of this study is to develop lahar hazard vulnerability as a warning system by introducing radar-rainfall observation to data mining technique of Naïve Bayes Classifier (NBC). NBC is used to estimate lahar occurrences based on the posterior probability of rainfall, topographic factor, soil moisture, and soil type as predictors. Rainfall intensity and working rainfall were obtained from a weather radar. The soil moisture is derived from SMAP satellite imagery. A river on Mount Merapi, a very active volcano in Indonesia, was selected as the target basin. Observed rainfall and recorded lahar events in Gendol River from October 2016 to February 2018 were divided into a training dataset and a testing dataset. Qualitative evaluation through visual assessment of the hazard map product reveals that the model could estimate the occurrences of lahar. The performance of the model in terms of accuracy, Brier score, and quantitative dichotomous quality indices showed a reasonable skill. The study suggests that the NBC technique is advantageous for estimating lahar occurrences that are displayed on hazard maps. This work is expected to contribute to debris flow hazard mitigation by the data mining approach in volcanic regions.

Geotechnical profiles of expansive soil hazard for road infrastructure: Case study of Takalar-Jeneponto provincial road corridor

This paper presents a developed geotechnical profiles which can be utlized to map expansive soil hazard for road infrastructure. The geotechnical profiles are a fundamental part of newly framework of establishing expansive soil hazard mapping which comprise of digital elevation model of area along road corridor, geological maps and geotechnical database consisting of measured in-situ and laboratory tests data, and predicted active zone by using rainfall and potential evapotranspiration data. The geotechnical profiles in the study area suggested that clay layer dominates soil formation in the area which is found to be very expansive. The thickenss of the clay layer varies due to geological structure where it is shallow in the anticline structure and deep in the sycline structure. Active zone is difficult to predict since the measurement of groundwater table is not time lapse measurement. Therefore, future study would investigate the use of SMOS and SMAP satellite platforms, and differential SAR inferometry would enhance prediction of spatial distribution of active zone of expansive soil.

Distribution of Desalinated Waters of the Amur Estuary in the Okhotsk Sea According to Satellite Observations

The distribution of the desalinated waters of the Amur Estuary in the Northwestern ​​Okhotsk Sea during the summer period was analyzed using satellite data on sea level, salinity, temperature and concentrations of suspended organic carbon. There is good agreement between the spatial distribution of surface water salinity (data from the SMAP satellite) and the sea level (AVISO data) in the study area. The identification of the waters of the Amur Estuary by the surface water temperature and the concentration of suspended organic carbon is limited to the Sakhalin Bay and the coastal waters of Sakhalin Island. An increase (decrease) in the flow of the Amur River during the spring-summer flood leads to an increase (decrease) in sea level in the northern part of the Sakhalin Bay in July. Strengthening of the northwestern (southeastern) winds in September reduces (increases) the inflow of waters with low salinity and elevated sea level to the area north of the Sakhalin Bay.

Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural region

Following results by Crow et al. (2017) [Geophys. Res. Lett. 44, 5495–5503] on the impact of antecedent soil moisture on runoff production, we investigate total runoff production during individual rainfall-runoff events in agricultural landscapes as a function of antecedent soil moisture, total rainfall, and vegetation cover for catchments with drainage areas ranging from 80 to 1000 km2 in the state of Iowa, USA. For our study, we use Enhanced SMAP soil moisture estimates, the MODIS enhanced vegetation index (EVI), gauge-corrected Stage IV radar rainfall, and USGS streamflow data. We analyze the event runoff ratio as a function of event-scale rainfall, antecedent SMAP soil moisture and soil-moisture-deficit-normalized rainfall for the events in a period from March 31, 2015 to October 31, 2018. Our goal is to confirm the relationships identified by Crow et al. (2017) in heavily managed agricultural landscapes and to refine some of their methodological steps to quantify the role of additional variables controlling runoff production. To this end, we define three different strategies to identify rainfall-runoff events and add a baseflow separation step to better insulate event scale stormflow runoff. We test the effects of antecedent soil moisture, rainfall, and vegetation on the event-scale runoff ratio. The antecedent SMAP soil moisture and event-scale rainfall are found to have significant predictive power in estimating event runoff ratio. Soil moisture deficit-normalized rainfall, introduced as the ratio of event-scale rainfall to available space in top soil before initiation of the event, exhibited a more distinct relationship with runoff ratio. The long-term analysis of runoff ratio, rainfall, and MODIS EVI indicated that, in an agricultural region, vegetation plays a significant role in determining event-scale runoff ratios. The methodology and outcome of our study have direct implications on real-time flood forecasting and long-term hydrologic assessments.

Estimation of relative canopy absorption and scattering at L-, C- and X-bands

Quantification of microwave vegetation properties (i.e. vegetation optical depth (VOD) and single scattering albedo (ω)) is of interest not only to reliably estimate soil moisture from the Earth's microwave emission, but also for broad applications across plant physiology and hydrology. Estimated VOD is often used as a biomass proxy or to investigate plant water dynamics on multiple scales, as it is dependent on the vegetation water content, dry woody biomass, and canopy structure. For most but not all microwave satellite remote sensing applications, VOD and ω are assigned a priori from auxiliary vegetation information and used as inputs to retrieve soil moisture. Alternatively, polarized brightness temperature has been used to estimate VOD and ω. Retrieval algorithms typically use the zeroth-order solution to the radiative transfer equation (tau-omega model) to simultaneously characterize surface and vegetation emission. In this study we investigate one year of estimated VOD and ω at L- (1.4 GHz), C– (6.9 GHz) and X-band (10.7 GHz), from SMAP and AMSR2 satellites respectively. Since VOD and ω describe absorption and scattering integrated over a path length, we derive estimates of relative absorption and scattering through normalization by a vegetation height derived from GLAS light detection and ranging (lidar) measurements.We assess whether relative attenuation is independent of vegetation height and land cover class. We also test how the relative absorption and scattering estimates follow a spectrum across microwave frequencies. We apply this analysis globally and find relative canopy absorption and scattering to follow distinct patterns, relatively independent of the spatial distributions of vegetation height. We find that relative canopy absorption generally peaks for shorter vegetation (5–10 m) and high density (shrub lands) while high forest canopies have relatively lower values. The VOD values that include the path length (taller vegetation) however are higher for forests relative to shorter vegetation. Seasonal amplitudes of VOD are higher for C- and X-band than for L-band and have maxima in regions with strong rainfall seasonality. This study presents first results for global relative canopy attenuation, based on one consistent retrieval for three frequencies, and discusses temporal dynamics as well as the role of multiple frequencies in assessing attenuation in canopies.

Sea surface salinity subfootprint variability estimates from regional high-resolution model simulations

Sea surface salinity (SSS) subfootprint variability (SFV) is estimated using high-resolution, realistically forced regional simulations of the Arabian Sea and western Pacific with an integration period of one year. A weighted standard deviation was calculated for footprint sizes of 100 km, 40 km, 20 km, and 10 km for all model time steps and then median (σ50) and 95th percentile (σ95) values were calculated along the time dimension. An additional method, wavenumber spectral analysis (σk), was also employed to obtain a different but comparable estimate. σ50 and σ95 maxima (>1 psu) are found in shallow waters along the continental shelves where strong river outflow is present. Open ocean values of both statistics are much lower (~0.1 psu). The wavenumber spectral analysis allowed the estimation of total SSS spatial variance over 640 km, which was then compared to the estimates obtained by integrating time-averaged SSS power spectral density (PSD) at wavelengths ≤100 km, 40 km, 20 km, and 10 km. For both geographic regions, the ratio of variance at and below each wavelength to the total variance across all estimated wavelengths is approximately 50%, 30%, 15%, and 5%, respectively. σ50, σ95, and σk magnitudes as a function of footprint size follow a power-law relationship. The observed strong decline in SSS SFV below 40 km suggests that the current effective resolution of the SMAP and SMOS satellites is advantageous for limiting the impact of SFV on the satellites' error budget.

Extending the SMAP 9-km soil moisture product using a spatio-temporal fusion model

SMAP satellite has provided us the first 9-km global soil moisture (SM) product, which is retrieved from the combined L-band radiometer and radar observations with a balance between accuracy and resolution. However, SMAP's radar failed on July 7, 2015, making the continuous production of the 9-km SM impossible, which has considerably affected the application of SMAP in hydrological monitoring. This study was aimed at extending the SMAP 9-km SM by developing a non-local filter based spatio-temporal fusion model (STFM). With the auxiliary of the historical 9-kmand 36-km products, the STFM was used to downscale the daily 36-km product to 9-km. Two-year 9-km product was estimated using STFM in the study. It shows that the estimated product has the detailed information retention of 9-km product and the comparable accuracy with 36-km product, which makes it feasible to improve the application potential of the current SMAP SM products.

Data-driven stochastic model for basin and sub-grid variability of SMAP satellite soil moisture

This study focused on the utility of coarse surface soil moisture observations for applications that require high resolution surface soil moisture information. This was accomplished by quantifying the information content of average soil moisture for three different spatial scales of 81 km2, 790 km2, and 4400 km2. In situ point observations of soil moisture from 31 stations in Iowa were used to develop a spatial stochastic model that assumes hillslope-scale model parameters are independent. Soil moisture dry-downs and wetting regimes were analyzed using rain gauge and soil moisture sensor data. The statistical nature of dry-downs were parameterized using a power-law decay, and soil moisture increases due to rainfall were parameterized using a non-dimensional logistic curve that is a function of soil moisture deficit. The resulting stochastic model is used to quantify the magnitude of the standard deviation, σ(θ), and skewness G(θ) as a function of the areal average. We show that the greatest information content (small spatial standard deviation) of average observation corresponded to values near the minimum or the maximum soil moisture with σ(θ)<5%, while average observations for intermediate soil moisture values had the lowest information content with σ(θ)>20%. The differences in information content as a function of the areal average were consistent with the statistical nature of soil moisture that can be interpreted as small range bounded variable. However, this study provides quantitative estimates for the magnitude of the sub-grid and basin scale variability, documenting the utility for applications that require high resolution information. These results form the basis for the investigation of spatial runoff production in response to rainfall and to inform plot scale agriculture applications.

Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China

The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy. Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP, SMOS, AMSR2, FY3B, and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture. We further evaluated our product, the official soil moisture products of the five satellites, and the ensemble mean (i.e., arithmetic mean) of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province, China. The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas. The root mean square error (RMSE) values of our product were 0.06 and 0.09 m3/m3 in Central Tibet and Anhui Province, respectively. Relative to the ensemble mean of the five satellite products, our product improves the accuracy by 9.1% and 57.7% in Central Tibet and Anhui Province, respectively. This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.

The distribution of the desalinated waters of the amur estuary in the okhotsk sea according to satellite observations

The distribution of the desalinated waters of the Amur Estuary in the Northwestern ​​Okhotsk Sea during the summer period was analyzed using satellite data on sea level, salinity, temperature and concentrations of suspended organic carbon. There is good agreement between the spatial distribution of surface water salinity (data from the SMAP satellite) and the sea level (AVISO data) in the study area. The identification of the waters of the Amur Estuary by the surface water temperature and the concentration of suspended organic carbon is limited to the Sakhalin Bay and the coastal waters of Sakhalin Island. An increase (decrease) in the flow of the Amur River during the spring-summer flood leads to an increase (decrease) in sea level in the northern part of the Sakhalin Bay in July. Strengthening of the northwestern (southeastern) winds in September reduces (increases) the inflow of waters with low salinity and elevated sea level to the area north of the Sakhalin Bay.

Study of Seasonal Variation of Sea Surface Salinity in Java Sea and its Surrounding Seas using SMAP Satellite

This research was conducted in The Java Sea and Surrounding Seas. This study uses satellite data with the period in 2015 - 2017, which composed of the main data of sea surface salinity and supporting data of rainfall, evaporation, wind, and current. The entire data processed into monthly climatology data and analyzed by using Pearson correlation. The salinity of Java Sea is generally in the range of 30.0 to 35.0 ‰. However, we found some phenomena in four areas, that is the significant decrease of salinity reach up to 30.919 ‰ in April in south waters of Borneo. Those influenced by river run off from the high precipitation reach up to 0.689 mm/hr in Borneo land. In south waters of Sulawesi, there is the highest increase of salinity in October to November reach up to 34.45 ‰ which affected by Banda Sea’s water mass entering the area. Salinity fluctuation in Natuna Sea is inversely proportional to other study areas, influenced by the geographical condition in the Northern Hemisphere which was crossed east monsoon and by the minimum salinity of water mass from the Java Sea which moved by the current which has correlation around of -0.717 between salinity and current. Negative of the high value shown big inverse relation. While the phenomenon of decreasing salinity is occurring in West Sulawesi waters in June, it is influenced by mass input from Banda Sea that depresses Java Sea waters and ARLINDO (Indonesia cross flow) which had minimum salinity from the west season.

Desert locust detection using Earth observation satellite data in Mauritania

Desert locust plagues have threatened food security in northern African countries for centuries. To prevent their effects, current early warning systems in arid environments need to be improved using the latest and most advanced modelling techniques and Earth observation datasets. Previous studies have analysed certain environmental predictors such as NDVI or soil moisture individually in an effort to detect suitable areas. However, we introduce new variables (Surface Temperature, LAI and Soil Moisture Root Zone) from the SMAP satellite and apply different machine learning methods in our species distribution model in order to identify desert locust presence. We obtain highly satisfactory model results (KAPPA & TSS = 0.901 and ROC = 0.986) to detect the probability of presence and, hence, likely breeding areas based on environmental factors. The most relevant variables were surface temperature, NDVI and soil moisture at root zone under different time scenarios. This study also confirms the potential of the SMAP satellite to retrieve critical temperatures due to its time pass, in addition to reinforcing the NDVI product from MODIS as a reliable environmental predictor. These results demonstrate the validity of this new approach based on machine learning methods to identify favourable breeding areas in Mauritania.

Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model

AbstractIrrigation representation in land surface models has been advanced over the past decade, but the soil moisture (SM) data from SMAP satellite have not yet been utilized in large‐scale irrigation modeling. Here we investigate the potential of improving irrigation representation in the Community Land Model version‐4.5 (CLM4.5) by assimilating SMAP data. Simulations are conducted over the heavily irrigated central U.S. region. We find that constraining the target SM in CLM4.5 using SMAP data assimilation with 1‐D Kalman filter reduces the root‐mean‐square error of simulated irrigation water requirement by 50% on average (for Nebraska, Kansas, and Texas) and significantly improves irrigation simulations by reducing the bias in irrigation water requirement by up to 60%. An a priori bias correction of SMAP data further improves these results in some regions but incrementally. Data assimilation also enhances SM simulations in CLM4.5. These results could provide a basis for improved modeling of irrigation and land‐atmosphere interactions.

Characterization of higher-order scattering from vegetation with SMAP measurements

Vegetation cover absorbs and scatters L-band microwave emission measured by SMOS and SMAP satellites. Misrepresentation of this phenomena results in uncertainties when inferring, for instance, surface soil moisture in retrieval algorithms that commonly utilize the tau-omega model which is most applicable for a weakly scattering medium. In this study, we investigate the degree to which multiple-scattering is prevalent over a range of land cover classifications (from lightly vegetated grasslands to dense forests) at the satellite scale by explicitly accounting for multiple-scattering in a first-order radiative transfer model, developed here. Even though the tau-omega model with effective parameters can possibly capture higher-order scattering contributions, deliberately partitioning scattering into different components is required to estimate multiple-scattering properties. Specifically, we aim to determine how one can partition between zeroth and first-order radiative transfer terms within a retrieval algorithm without ancillary information, determine whether this method can detect first-order scattering at the SMAP measurement scale without ancillary information, and quantify the magnitude of detected scattering. A simplified first-order radiative transfer model which characterizes single interactions of microwaves with a scattering medium is developed for implementation within retrieval algorithms. This new emission model is implemented within a recently developed retrieval algorithm, the multi-temporal dual channel algorithm (MT-DCA), which does not require ancillary land use information. Scattering parameters as well as SM and vegetation optical depth (τ) are retrieved simultaneously in Africa and South America using the first year of SMAP brightness temperature measurements on a 36 km grid. Specifically, an introduced time invariant first-order scattering coefficient (ω1) is retrieved representing microwave emission interaction with the canopy. We find that ω1 is typically zero in lightly vegetated biomes and non-zero (~0.06) in 74% of the forest pixels. In forest-dominated pixels, the median first-order emissivity is 0.04, or about 4.3% of a given SMAP radiometer brightness temperature measurement. Additionally, explicitly accounting for first-order scattering terms in the radiative transfer model tends to increase SM and τ retrievals by a median of 0.02 m3/m3 and 0.1, respectively, only in forested regions. This study demonstrates the first attempt to explicitly partition higher-order scattering terms in a retrieval algorithm at a satellite scale and ultimately provides a fundamental understanding and quantification of multiple-scattering from grasslands to forests.

Integration of microwave data from SMAP and AMSR2 for soil moisture monitoring in Italy

In this study, an integration of microwave data obtained from the SMAP and AMSR2 satellite radiometers has been attempted, to achieve an accurate estimation of the Soil Moisture Content (SMC). This research aimed to overcome the failure of radar sensor in SMAP satellite as well as the failure to generate the radar/radiometer combined SMC product at a spatial resolution of 9 km × 9 km.A disaggregation technique, based on the Smoothing Filter based Intensity Modulation (SFIM), enabled us to obtain co-located SMAP and AMSR2 brightness measurements at L, C, X, Ku and Ka bands at approximately 10 km × 10 km on the selected test area, which corresponds to the entire Italian territory. These disaggregated microwave data were used as inputs of the “HydroAlgo” retrieval algorithm based on Artificial Neural Networks (ANN), which were able to exploit the synergy between radiometric acquisitions from these two sensors. The algorithm was defined, implemented and tested using all the overlapping orbits of SMAP and AMSR2 over Italy throughout the 9_month period between April and December 2015. Distributed SMC reference values for implementing and validating the algorithm were obtained from the Soil Water Balance hydrological model, SWBM. Through HydroAlgo, an SMC product at a resolution of approximately 10 km × 10 km was obtained. This result is close to the original Radar/Radiometer SMC product from SMAP, with an average correlation coefficient R > 0.75 and RMSE ≅ 0.03 m3/m3, in both ascending and descending orbits.

Evaluating Consistency between the Remotely Sensed Soil Moisture and the Hydrological Model-Simulated Soil Moisture in the Qujiang Catchment of China

Accurate soil moisture estimation plays a crucial role in agricultural management and hydrological studies. Considering the scarcity of direct in-situ measurements, it is important to evaluate the consistency of soil moisture data acquired in indirect ways, including both satellite products and simulation values obtained via hydrological models. In this study, two types of high spatial-resolution remotely sensed values, namely the surface soil moisture (SSM) and the profile soil water index (SWI), are estimated from each of the ASCAT-A, ASCAT-B, SMAP and SMOS microwave satellites. They are compared with two groups of model-simulated daily soil moisture values, which are obtained by implementing the lumped Xinanjiang (XAJ) model and the DEM-based distributed hydrological model (DDRM) across the Qujiang catchment, located in southwest China. The results indicate that for each satellite product, SWI values always show closer agreement with model-simulated soil moisture values than SSM values, and SWI values estimated from ASCAT products perform best in terms of correlation coefficient with the model-simulated soil moisture, at around 0.8 on average, followed by the SMAP product, which shows a correlation coefficient of 0.48 on average, but the SMOS product shows poor performance. This evaluation of consistency provides useful information on their systematic differences and suggests subsequent studies to ensure their reconciliation in long-term records.

Ingestion of Simulated SMAP L3 Soil Moisture Data into Military Maneuver Planning

Abstract This paper uses simulated SMAP level-3 (L3) soil moisture data to calculate soil strength directly and compares the results against the current Noah Land Information System–based climatology approach. Based on the availability of data, three sites were chosen for the study: Cheorwon, South Korea; Laboue, Lebanon; and Asham, Nigeria. The simulated SMAP satellite data are representative of May conditions. For all three regions, this is best represented by the “average” soil moisture used in the current climatology approach. The cumulative distribution frequency of the two soil moisture sources indicates good agreement at Asham, Nigeria; mixed agreement at Cheorwon, South Korea; and no agreement at Laboue, Lebanon. Soil strengths and resulting vehicle speeds for a High Mobility Multipurpose Wheeled Vehicle (HMMWV) M1097 were calculated based on the Harmonized World Soil Database soil types used by the two soil moisture sources, as well as with a finer-resolution National Geospatial-Intelligence Agency product. Better agreement was found in soil strengths using the finer-resolution soil product. Finally, fairly large differences in soil moisture become muted in the speed calculations even when all factors except soil strength, slope, and vehicle performance are neglected. It is expected that the 0.04 volumetric uncertainty in the final SMAP L3 soil moisture product will have the greatest effect at low vehicle speeds. Field measurements of soil moisture and strength as well as soil type are needed to verify the results.