Near-surface Soil Moisture Research Articles

The Potential Evaluation of Multisource Remote Sensing Data for Extracting Soil Moisture Based on the Method of BP Neural Network

. The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) conducted in Manitoba, Canada, over a 6-week period in 2012 provided high-quality data for soil moisture and vegetation, captured over a wide range of conditions coincident with airborne active and passive microwave acquisitions. In this study, the dataset measured in SMAPVEX12 was used as the input data of the backpropagation (BP) neural network to analyze the potential of different data sources or combination of data sources in the research of soil moisture inversion. By the analysis and comparison of the results, some valuable conclusions have been obtained: (1) passive microwave data has a stronger and more direct connection with the near-surface soil moisture (top 0∼6 cm) than the active microwave sensing (radar backscatter coefficient) does, because the active microwave is more sensitive to the confounding effects of vegetation and surface roughness. (2) For the active microwave data, some combination form of the horizontal transmit and horizontal receive (HH) and vertical transmit and horizontal receive (VH) is a more rational selection than only HH to serve as one of the independent information sources for extracting the soil moisture. (3) The optical vegetation information provides the rich vegetation information that is indispensable for the microwave remote sensing data, especially for passive remote sensing, to extract the soil moisture with the average correlation coefficient increased by more than 0.05, at least, when adding the optical vegetation data. (4) Two kinds of forms of the optical vegetation information, either Vegetation Water Content (VWC) or Normalized Difference Water Index (NDWI), show no obvious differences to provide as auxiliary information for microwave remote sensing data in extracting soil moisture.

Surface energy budget changes over Central Australia during the early 21st century drought

Satellite observations are used to investigate surface energy budget variability over central Australia during the early 21st century drought. Over a large expanse of open shrubland and savanna, surface albedo exhibits a multiyear increase of 0.06 during the drought followed by a sharp decline of 0.08 after heavy rainfall in 2010 broke the drought. The surface albedo variations are associated with increased normalized difference vegetation index (NDVI) during wet years before and after the drought and decreased NDVI during drought years. During the worst drought years (2002–2009), the surface albedo increase is most pronounced in the shortwave infrared region (wavelengths between 1 and 3 µm), implying soil moisture content variability is the likely cause of the albedo changes. At interannual timescales, surface albedo variability is associated with near-surface soil moisture, controlled by episodic precipitation events, whereas the multiyear increase in surface albedo is more closely linked with decreases in soil moisture in deeper surface layers. In addition to a higher surface albedo and lower soil moisture content during the drought, the observations show less evaporation, enhanced reflected shortwave radiation, increased upward emission of thermal infrared radiation, lower downwelling longwave (LW) radiation, reduced net total downward radiation, and higher sensible heating compared with the rainy period following the drought. Upward emission of thermal infrared radiation decreases sharply after the drought with increased surface evaporation. However, the surface energy budget changes during the worst drought years show a stronger relationship between upward emission of thermal radiation and reflected shortwave flux. During this period, evaporative fraction is extremely low and surface albedo is steadily increasing. In such extreme conditions, the surface albedo appears to modulate surface upward LW radiation, preventing it from getting too high. The change in upward LW radiation thus represents a negative feedback as it offsets further decreases in surface net radiation.

Regional Soil Moisture Biases and Their Influence on WRF Model Temperature Forecasts over the Intermountain West

Abstract Operational Weather Research and Forecasting (WRF) Model forecasts run over Dugway Proving Ground (DPG) in northwest Utah, produced by the U.S. Army Test and Evaluation Command Four-Dimensional Weather System (4DWX), underpredict the amplitude of the diurnal temperature cycle during September and October. Mean afternoon [2000 UTC (1300 LST)] and early morning [1100 UTC (0400 LST)] 2-m temperature bias errors evaluated against 195 surface stations using 6- and 12-h forecasts are –1.37° and 1.66°C, respectively. Bias errors relative to soundings and 4DWX-DPG analyses illustrate that the afternoon cold bias extends from the surface to above the top of the planetary boundary layer, whereas the early morning warm bias develops in the lowest model levels and is confined to valleys and basins. These biases are largest during mostly clear conditions and are caused primarily by a regional overestimation of near-surface soil moisture in operational land surface analyses, which do not currently assimilate in situ soil moisture observations. Bias correction of these soil moisture analyses using data from 42 North American Soil Moisture Database stations throughout the Intermountain West reduces both the afternoon and early morning bias errors and improves forecasts of upper-level temperature and stability. These results illustrate that the assimilation of in situ and remotely sensed soil moisture observations, including those from the recently launched NASA Soil Moisture Active Passive (SMAP) mission, have the potential to greatly improve land surface analyses and near-surface temperature forecasts over arid regions.

Impact of Bias Correction Methods on Estimation of Soil Moisture When Assimilating Active and Passive Microwave Observations

In this paper, bias correction approaches are investigated to understand their impact on assimilating active and/or passive microwave observations on near-surface soil moisture (SM) estimates. Synthetic and field observations were assimilated in a soil–vegetation–atmosphere transfer model linked with an integrated active–passive model at L-band for bare soil. The two bias correction methods included in this study are the online bias correction with feedback (BCWF) with extended implementation with nonlinear observation operators and the simultaneous state parameter (SSP) update. New equations for BCWF were derived for the case of nonlinear observation operators because current versions of this approach were not applicable for improving SM by assimilating microwave observations. In SSP, the bias is compensated by tunning the values of the parameters. The two approaches resulted in similar accuracy for improving SM estimates compared with the uncorrected estimates. SSP showed the highest certainty for both synthetic and field observations. Using the bias correction methods, the mean estimates of SM improved by up to 88%, 87%, and 94%, when passive, active, and active–passive synthetic observations were assimilated, respectively, compared with the open-loop estimates. In contrast, when assimilating field observations from the Eleventh Microwave Water Energy Balance Experiment, the mean estimates of SM improved by up to 44%, 18%, and 48%, when passive, active, and active–passive observations were assimilated, respectively, compared with open-loop estimates. The decrement in improving the SM estimates suggests sources of uncertainty other than those from model parameters and forcings.

How Has Human-Induced Climate Change Affected California Drought Risk?

Abstract The current California drought has cast a heavy burden on statewide agriculture and water resources, further exacerbated by concurrent extreme high temperatures. Furthermore, industrial-era global radiative forcing brings into question the role of long-term climate change with regard to California drought. How has human-induced climate change affected California drought risk? Here, observations and model experimentation are applied to characterize this drought employing metrics that synthesize drought duration, cumulative precipitation deficit, and soil moisture depletion. The model simulations show that increases in radiative forcing since the late nineteenth century induce both increased annual precipitation and increased surface temperature over California, consistent with prior model studies and with observed long-term change. As a result, there is no material difference in the frequency of droughts defined using bivariate indicators of precipitation and near-surface (10 cm) soil moisture, because shallow soil moisture responds most sensitively to increased evaporation driven by warming, which compensates the increase in the precipitation. However, when using soil moisture within a deep root zone layer (1 m) as covariate, droughts become less frequent because deep soil moisture responds most sensitively to increased precipitation. The results illustrate the different land surface responses to anthropogenic forcing that are relevant for near-surface moisture exchange and for root zone moisture availability. The latter is especially relevant for agricultural impacts as the deep layer dictates moisture availability for plants, trees, and many crops. The results thus indicate that the net effect of climate change has made agricultural drought less likely and that the current severe impacts of drought on California’s agriculture have not been substantially caused by long-term climate changes.

The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method

Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation.

Evaluation of AMSR2 soil moisture products over the contiguous United States using in situ data from the International Soil Moisture Network

High quality soil moisture datasets are required for various environmental applications. The launch of the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission 1—Water (GCOM-W1) in May 2012 has provided global near-surface soil moisture data, with an average revisit frequency of two days. Since AMSR2 is a new passive microwave system in operation, it is very important to evaluate the quality of AMSR2 products before widespread utilization of the data for scientific research. In this paper, we provide a comprehensive evaluation of the AMSR2 soil moisture products retrieved by the Japan Aerospace Exploration Agency (JAXA) algorithm. The evaluation was performed for a three-year period (July 2012–June 2015) over the contiguous United States. The AMSR2 soil moisture products were evaluated by comparing ascending and descending overpass products to each other as well as comparing them to in situ soil moisture observations of 598 monitoring stations obtained from the International Soil Moisture Network (ISMN). The accuracy of AMSR2 soil moisture product was evaluated against several types of monitoring networks, and for different land cover types and ecoregions. Three performance metrics, including mean difference (MD), root mean squared difference (RMSD), and correlation coefficient (R), were used in our accuracy assessment. Our evaluation results revealed that AMSR2 soil moisture retrievals are generally lower than in situ measurements. The AMSR2 soil moisture retrievals showed the best agreement with in situ measurements over the Great Plains and the worst agreement over forested areas. This study offers insights into the suitability and reliability of AMSR2 soil moisture products for different ecoregions. Although AMSR2 soil moisture retrievals represent useful and effective measurements for some regions, further studies are required to improve the data accuracy.

A Cumulative Distribution Function Method for Normalizing Variable-Angle Microwave Observations

Microwave remote sensing has been widely acknowledged as the most promising technique to measure the spatial distribution of near-surface soil moisture. However, due to a strong incidence angle dependence in microwave radiometer and radar data, airborne observations typically have an across-track variation in incidence angle that needs to be normalized to a fixed angle for the purposes of data visualization and aggregation to spatial resolutions that mimic spaceborne data. There are two normalization methods commonly used, often resulting in a noticeable stripe pattern along the flight direction. This paper develops a 2-D cumulative distribution function (CDF)-based normalization method, which normalizes the variable-angle observations to a reference angle by matching the CDF of observations for each nonreference angle, using the information content from multiple partially overlapped swaths. The performance of this method is tested using an airborne microwave radiometer and radar observations collected during three Australian field experiments. The normalization results show that the stripe pattern problem over heterogeneous land surfaces when not any prior knowledge of land surface types is primarily attributed to the linearity of the commonly used normalization methods, and that the nonlinear 2-D CDF-based method produced the least noticeable stripe pattern and the highest normalization accuracy when compared with independent data. Compared with the two linear methods, a root-mean-squared error improvement of up to 2 K was obtained using 1-km radiometer data, and a correlation coefficient improvement of 0.2 and RMSE improvement of ~0.2 dB were achieved for the 7-m resolution radar data.

Droughts and Floods in the La Plata Basin in Soil Moisture Data and GRACE

The mapping and forecasting of droughts and floods is an important potential field of application of global soil moisture and water storage products from satellites and models. Especially when extremes in near-surface soil moisture propagate into extremes in total water storage, agricultural production and water supply can be severely impacted. This study relates soil moisture from the WaterGAP Global Hydrology Model (WGHM) and the satellite sensors Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) and Advanced Scatterometer (ASCAT) to total water storage variations from the satellite gravity mission GRACE. A particular focus is on destructive hydrological extreme events, as listed in the International Disaster Database EM-DAT. Data sets are analyzed via correlation, time shift, and principal component analyses. The study area is the La Plata Basin in South America. The results indicate that most of the soil moisture anomalies are linked to periods of El Niño and La Niña and associated natural disasters. For the La Plata drought of 2008/2009 and the El Niño flooding of 2009/2010, soil moisture serves as an indicator for the later deficit or surplus in total water storage. These hydrological anomalies were strongest in the southern, central, and eastern parts of the basin, but more than one hundred thousand people were also affected in the northwestern part.

An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil

Ground-reflected global positioning system signals measured by a geodetic-quality GPS system can be used to infer temporal changes in near-surface soil moisture for the area surrounding the antenna...

Retrieving vegetation growth patterns from soil moisture, precipitation and temperature using maximum entropy

This study employs entropy theory to evaluate the relation of vegetation cover to soil moisture, precipitation and temperature patterns in the Texas Gulf watershed. Over a 12-year period, we consider the Normalized Differential Vegetation Index (NDVI) of the growing season (May to September) for deciduous forest and grasslands, as well as precipitation, temperature, and soil moisture data at a biweekly time scale. Using three different vegetation growth metrics, we analyze patterns in vegetation responses. An entropy scaling of the system of vegetation-soil moisture-precipitation-temperature reveals trends toward maximum entropy and shows the relevance of coupling these atmospheric variables in vegetation dynamic analysis. Our analysis indicates that soil moisture is potentially efficient to use for vegetation dynamics monitoring at finer time scales compared to precipitation. The near-surface (5cm) soil moisture series shows meaningful relationship with vegetation growth series. This seems interesting, as the recent satellite soil moisture monitoring projects are designed for estimating near-surface moisture. Month-wise, the vegetation response to atmospheric variables shows important dissimilarities. Therefore, we use an entropy-based clustering approach to discriminate the growing season. Later, we propose a nested statistical model for retrieving an estimate of NDVI. We find that the inclusion of soil moisture and temperature explains up to 68% and 62% of the variation of NDVI, respectively, for deciduous forest and grassland during June and July. However, these relationships appear weaker during the end of the growing season (August and September). The outcomes of the study are suitable for ecosystem monitoring under the realm of climate change. Likewise, the techniques employed may find useful applications for water resources management at the scale of large watersheds. Nevertheless, further studies on the topic are necessary and should involve diversified ecosystems and remote sensed products.

Different Rates of Soil Drying after Rainfall Are Observed by the SMOS Satellite and the South Fork in situ Soil Moisture Network

Abstract Soil moisture affects the spatial variation of land–atmosphere interactions through its influence on the balance of latent and sensible heat fluxes. Wetter soils are more prone to flooding because a smaller fraction of rainfall can infiltrate into the soil. The Soil Moisture Ocean Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near-surface soil moisture on a global scale. One way to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. The rate of soil drying after significant rainfall observed by SMOS is found to be higher than the rate observed by a U.S. Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork Iowa River. This leads to the conclusion that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few centimeters thick, while the network observes a deeper soil layer centered at the depth at which the in situ soil moisture sensors are buried. It is also found that SMOS near-surface soil moisture is drier than the South Fork network soil moisture, on average. The conclusion that SMOS and the network observe different layers of the soil, and therefore different soil moisture dynamics, cannot explain the dry bias. However, it can account for some of the root-mean-square error in the relationship. In addition, SMOS observations are noisier than the network observations.

Analysis of spatial variability of near-surface soilmoisture to increase rainfall-runoff modellingaccuracy in SW Hungary

Abstract Between September 5, 2008 and September 5,2009, near-surface soil moisture time series were collectedin the northern part of a 1.7 km2 watershed in SWHungaryat 14 monitoring locations using a portable TDR-300 soilmoisture sensor. The objectives of this study are to increasethe accuracy of soil moisture measurement at watershedscale, to improve flood forecasting accuracy, and to optimizesoil moisture sensor density. According to our results, in 10 of 13 cases, a strong correlationexists between the measured soil moisture dataof Station 5 and all other monitoring stations; Station 5is considered representative for the entire watershed. Logically,the selection of the location of the representativemeasurement point(s) is essential for obtaining representativeand accurate soil moisture values for the given watershed.This could be done by (i) employing monitoringstations of higher number at the exploratory phase of themonitoring, (ii) mapping soil physical properties at watershedscale, and (iii) running cross-relational statisticalanalyses on the obtained data. Our findings indicate that increasing the number of soilmoisture data points available for interpolation increasesthe accuracy of watershed-scale soil moisture estimation.The data set used for interpolation (and estimation ofmean antecedent soil moisture values) could be improved(thus, having a higher number of data points) by selectingpoints of similar properties to the measurement pointsfrom the DEM and soil databases. By using a higher numberof data points for interpolation, both interpolation accuracyand spatial resolution have increased for the measuredsoil moisture values for the Pósa Valley.

Measured and Modeled Soil Moisture Compared with Cosmic‐Ray Neutron Probe Estimates in a Mixed Forest

Soil moisture is a key variable in most environmental processes, and the cosmic-ray neutron probe (CRNP) fills a niche for intermediate-scale soil moisture measurements. In this study, the CRNP estimated soil moisture was compared with a soil moisture measurement network including 108 time domain transmissometry (TDT) probes. We also used a Hydrus-1D numerical model of measured soil moisture at targeted locations by inversely fitting soil hydraulic parameters used to simulate soil moisture in the near surface (0.03 and 0.05 m) during the growing seasons of 2011 and 2012. Both simulated and TDT-measured soil moisture were used in constructing the depth-weighted mean areal soil moisture for comparison with the CRNP estimates. The results showed that near-surface soil moisture estimated by the numerical simulation improved the correlation between the sensor network and the CRNP estimation, especially during rainfall events. The CRNP estimates of soil moisture exhibited a dry bias under relatively wet conditions at the beginning of the snow-free period because of the almost binary spatial distribution of soil moisture. Using a combination of soil moisture measurements and near-surface simulations, the CRNP output was recalibrated to capture the wetter conditions, resulting in a RMSE (0.011 m3/m3) of less than half the original calibration RMSE (0.025 m3/m3). The calibration was validated using CRNP data from the 2013 growing season against independent soil moisture values.

Evaluation of near-surface soil moisture data from an AAFC monitoring network in Manitoba, Canada: Implications for L-band satellite validation

Summary This paper examines near-surface soil moisture data (top ∼6 cm) from an Agriculture and Agri-food Canada (AAFC) monitoring network in southern Manitoba, Canada, over the 2012 and 2013 growing seasons. This network is intended for use (in part) as a core validation site for the Soil Moisture Active Passive (SMAP) mission. The network currently consists of 9 stations sited within agricultural fields. Each station uses a triplicate configuration of horizontally installed Hydra Probe soil moisture sensors at near-surface (∼5 cm) to root-zone soil depths, in addition to permanently installed vertical surface sensors (0–5.7 cm). This domain was studied intensively over the 6.5 week SMAPVEX12 ground campaign in June/July 2012, providing coincident soil moisture datasets to assess network up-scaling over the domain. Results demonstrate that statistical differences exist between the network-level data from the horizontal ( θ 3.5 – 6.5cm ) and vertical ( θ 0 – 5.7cm ) surface Hydra Probes over 2012–13. Bootstrap resampling shows that a mean difference of 0.018 m 3 m −3 (at 99% confidence) is observed between θ 3.5 – 6.5cm and θ 0 – 5.7cm measurement depths ( θ 0 – 5.7cm sensors exhibit dry bias). Individual station analysis shows that the largest differences between θ 3.5 – 6.5cm and θ 0 – 5.7cm measurement depth occur at sites with higher clay content, whereas sensors located in sandy soils exhibit negligible differences. Up-scaling comparisons of the AAFC network to field-sampled data and a dense temporary network over SMAPVEX12 demonstrate that it is representative of the domain within 0.04 m 3 m −3 RMSE. This suggests that the AAFC network is suitable for coarse resolution L-band microwave soil moisture validation. Comparison of Soil Moisture Ocean Salinity (SMOS) Level 2 soil moisture product data to the up-scaled AAFC network data over 2012–13 shows that superior temporal correlation is found with the θ 0 – 5.7cm dataset. The difference in RMSEs between the SMOS comparisons to θ 3.5 – 6.5cm and θ 0 – 5.7cm network datasets is approximately 0.018 m 3 m −3 . These results suggest that differences of near-surface measurement depth and sensor configuration should be considered when conducting L-band satellite validation.

Incorporating Surface Soil Moisture Information in Error Modeling of TRMM Passive Microwave Rainfall

This study assesses the significance of conditioning the error modeling of The National Aeronautics and Space Administration (NASA)'s Tropical Rainfall Measurement Mission Microwave Imager rainfall algorithm (2A12) to near-surface soil moisture data derived from a land surface model. The term “conditioning” means the model parameters' dependence on soil wetness. The Oklahoma (OK) region is used as the study area due to its relatively low vegetation and smooth terrain and the availability of high-quality in situ hydrometeorological data from the Mesonet network. The study period includes two warm seasons (March to October) from 2009 and 2010. The National Oceanic and Atmospheric Administration/National Severe Storms Laboratory ground radar-based National Mosaic and Quantitative Precipitation Estimation system (NMQ/Q2) is used as high-resolution (5-min/1-km) reference rainfall. The surface wetness conditions (wet, dry, and normal) were determined from surface soil moisture fields simulated by the NASA Catchment Land Surface Model forced with Q2 rainfall fields. A 2-D satellite rainfall error model, SREM2D, is used to provide the ensemble error representation of 2A12 rainfall using two different parameter calibration approaches: conditioning the SREM2D parameters to the surface soil wetness categories versus not. The statistical analysis of model-generated ensembles and associated error metrics show better performance when surface wetness information is used in SREM2D. In terms of quantification, the ensemble rainfall from the conditional SREM2D parameter calibration shows better reference rainfall encapsulation. The conditioning of SREM2D to soil wetness can apply to rainfall rate estimates from other microwave sensors on board low Earth orbiting satellites and is valuable for the forthcoming missions on precipitation (Global Precipitation Measurement) and soil moisture (Soil Moisture Active Passive).

A Model of Surface Roughness for Use in Passive Remote Sensing of Bare Soil Moisture

Spaceborne radiometers operating near 1.4 GHz are the primary instrument for recent efforts to remotely sense nearsurface soil moisture around the globe. Generally, these instruments must contend with the effects of vegetation growing in the soil. However, an important first step is to model the measurements made by a radiometer that is viewing bare (vegetation-free) soil. The proposed model uses a matching layer and a random depolarizer to describe bare soil surface roughness and some aspects of antenna beamwidth. The model suggests that the effects of nearsurface soil moisture and roughness upon the radiometer measurement are more distinct than is currently thought. Furthermore, it appears that both moisture and roughness can be retrieved from a single set of radiometer measurements made at orthogonal linear polarizations. This retrieval precision is predicted to be poor at soil observation angles near nadir but improves for larger angles. At observation angles near 50°, the vertically polarized radiometer measurements are predicted to be nearly insensitive to roughness. A convenient parameterization of the model is provided and permits quick implementation.

Field-scale moisture estimates using COSMOS sensors: A validation study with temporary networks and Leaf-Area-Indices

Summary The COsmic-ray Soil Moisture Observing System (COSMOS) is a new and innovative method for estimating surface and near-surface soil moisture at large (∼700 m) scales. This system accounts for liquid water within its measurement volume. Many of the sites used in the early validation of the system had low and stable vegetation water content. It is necessary to perform validation of COSMOS in a landscape with a significant change in vegetation water content to estimate its impact on the COSMOS estimate, and potentially, correct the estimate to account for the dynamic vegetation cover. A COSMOS station was installed in Beltsville, MD in the spring of 2012, within the domain of a long-term experimental station on USDA property. This station has a large array of soil moisture sensors in profile across the field, which is approximately 700 m in diameter. Frequent estimates of Leaf Area Index (LAI) were made throughout the 2012 and 2013 growing seasons (May through September). COSMOS readings, with a simple linear adjustment, were able to produce estimates of area-weighted averages of the existing in situ network with root-mean-squared-errors (RMSE) of soil-water content well below 0.04 m3/m3 and with additional modeling to account for Leaf Area Index (LAI) values, RMSE values less than 0.03 m3/m3 were achieved.

Characterizing coarse-resolution watershed soil moisture heterogeneity using fine-scale simulations

Abstract. Watershed-scale hydrological and biogeochemical models are usually discretized at resolutions coarser than where significant heterogeneities in topography, abiotic factors (e.g., soil properties), and biotic (e.g., vegetation) factors exist. Here we report on a method to use fine-scale (220 m grid cells) hydrological model predictions to build reduced-order models of the statistical properties of near-surface soil moisture at coarse resolution (25 times coarser, ~7 km). We applied a watershed-scale hydrological model (PAWS-CLM4) that has been previously tested in several watersheds. Using these simulations, we developed simple, relatively accurate (R2 ~0.7–0.8), reduced-order models for the relationship between mean and higher-order moments of near-surface soil moisture during the nonfrozen periods over five years. When applied to transient predictions, soil moisture variance and skewness were relatively accurately predicted (R2 0.7–0.8), while the kurtosis was less accurately predicted (R2 ~0.5). We also tested 16 system attributes hypothesized to explain the negative relationship between soil moisture mean and variance toward the wetter end of the distribution and found that, in the model, 59% of the variance of this relationship can be explained by the elevation gradient convolved with mean evapotranspiration. We did not find significant relationships between the time rate of change of soil moisture variance and covariances between mean moisture and evapotranspiration, drainage, or soil properties, as has been reported in other modeling studies. As seen in previous observational studies, the predicted soil moisture skewness was predominantly positive and negative in drier and wetter regions, respectively. In individual coarse-resolution grid cells, the transition between positive and negative skewness occurred at a mean soil moisture of ~0.25–0.3. The type of numerical modeling experiments presented here can improve understanding of the causes of soil moisture heterogeneity across scales, and inform the types of observations required to more accurately represent what is often unresolved spatial heterogeneity in regional and global hydrological models.