Soil Moisture Time Series Research Articles

Modeling Spatial Soil Water Dynamics in a Tropical Floodplain, East Africa

Analyzing the spatial and temporal distribution of soil moisture is critical for ecohydrological processes and for sustainable water management studies in wetlands. The characterization of soil moisture dynamics and its influencing factors in agriculturally used wetlands pose a challenge in data-scarce regions such as East Africa. High resolution and good-quality time series soil moisture data are rarely available and gaps are frequent due to measurement constraints and device malfunctioning. Soil water models that integrate meteorological conditions and soil water storage may significantly overcome limitations due to data gaps at a point scale. The purpose of this study was to evaluate if the Hydrus-1D model would adequately simulate soil water dynamics at different hydrological zones of a tropical floodplain in Tanzania, to determine controlling factors for wet and dry periods and to assess soil water availability. The zones of the Kilombero floodplain were segmented as riparian, middle, and fringe along a defined transect. The model was satisfactorily calibrated (coefficient of determination; R2 = 0.54–0.92, root mean square error; RMSE = 0.02–0.11) on a plot scale using measured soil moisture content at soil depths of 10, 20, 30, and 40 cm. Satisfying statistical measures (R2 = 0.36–0.89, RMSE = 0.03–0.13) were obtained when calibrations for one plot were validated with measured soil moisture for another plot within the same hydrological zone. Results show the transferability of the calibrated Hydrus-1D model to predict soil moisture for other plots with similar hydrological conditions. Soil water storage increased towards the riparian zone, at 262.8 mm/a while actual evapotranspiration was highest (1043.9 mm/a) at the fringe. Overbank flow, precipitation, and groundwater control soil moisture dynamics at the riparian and middle zone, while at the fringe zone, rainfall and lateral flow from mountains control soil moisture during the long rainy seasons. In the dry and short rainy seasons, rainfall, soil properties, and atmospheric demands control soil moisture dynamics at the riparian and middle zone. In addition to these factors, depths to groundwater level control soil moisture variability at the fringe zone. Our results support a better understanding of groundwater-soil water interaction, and provide references for wetland conservation and sustainable agricultural water management.

Spatially Distributed Evaluation of ESA CCI Soil Moisture Products in a Northern Boreal Forest Environment

Several previous studies have discussed the challenges in remotely sensed soil moisture retrievals over northern boreal environments. However, very few studies have focused solely on an evaluation of these products specifically over these areas. This study provides an in-depth evaluation of the European Space Agency’s (ESA) Climate Change Initiative (CCI) Soil Moisture (SM) product and its components; ACTIVE and PASSIVE soil moisture retrievals. The performance of a spatially distributed soil moisture model (SAC-SMA) is first validated with in situ observations collected from the Finnish Meteorological Institute’s (FMI) multidisciplinary research center near the town of Sodankylä, in Northern Finland. SAC-SMA model top soil layer moisture estimates are then used for spatially distributed ESA CCI SM product evaluation. The study domain covers an area of 155 km by 140 km. Evaluation is performed for thawed/snow-free periods between 2003 and 2015. The ACTIVE product exhibits high correlations with SAC-SMA soil moisture estimates during most analyzed years. The presence of high inter-pixel soil moisture time series cross-correlation, even between pixels with very different soil/vegetation type distributions, as well as the inconsistent performance between analyzed years, is problematic. The PASSIVE product is able to more consistently capture the trend in soil moisture variation; although the trend is seemingly captured, the rapid response to precipitation events is less accurate. Our results indicate that, in contrast to other previous studies, despite the challenges, the ESA CCI SM products do exhibit reasonably good performance, and that further improvements, even with current Earth Observation methods, may be possible.

Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

The objective of this research is to optimize the Alpha approximation model for soil moisture retrieval using multitemporal SAR data. The Alpha model requires prior knowledge of soil moisture range to constrain soil moisture estimation. The solution of the Alpha model is an undetermined problem due to the fact that the number of observation equations is less than the number of unknown parameters. This research primarily focused on the optimization of Alpha model by employing multisensor and multitemporal SAR data. The disadvantage of the Alpha model can be eliminated by the combination of multisensor SAR data. The optimized Alpha model was evaluated on the basis of a comprehensive campaign for soil moisture retrieval, which acquired multisensor time series SAR data and coincident field measurements. The agreement between the estimated and measured soil moisture was within a root mean square error of 0.08 cm3/cm3 for both methods. The optimized Alpha model shows an obvious improvement for soil moisture retrieval. The results demonstrated that multisensor and multitemporal SAR data are favorable for time series soil moisture retrieval over bare agricultural areas.

Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors

Abstract. Soil moisture plays a critical role in land surface processes and as such there has been a recent increase in the number and resolution of satellite soil moisture observations and the development of land surface process models with ever increasing resolution. Despite these developments, validation and calibration of these products has been limited because of a lack of observations on corresponding scales. A recently developed mobile soil moisture monitoring platform, known as the rover, offers opportunities to overcome this scale issue. This paper describes methods, results and testing of soil moisture estimates produced using rover surveys on a range of scales that are commensurate with model and satellite retrievals. Our investigation involved static cosmic-ray neutron sensors and rover surveys across both broad (36 × 36 km at 9 km resolution) and intensive (10 × 10 km at 1 km resolution) scales in a cropping district in the Mallee region of Victoria, Australia. We describe approaches for converting rover survey neutron counts to soil moisture and discuss the factors controlling soil moisture variability. We use independent gravimetric and modelled soil moisture estimates collected across both space and time to validate rover soil moisture products. Measurements revealed that temporal patterns in soil moisture were preserved through time and regression modelling approaches were utilised to produce time series of property-scale soil moisture which may also have applications in calibration and validation studies or local farm management. Intensive-scale rover surveys produced reliable soil moisture estimates at 1 km resolution while broad-scale surveys produced soil moisture estimates at 9 km resolution. We conclude that the multiscale soil moisture products produced in this study are well suited to future analysis of satellite soil moisture retrievals and finer-scale soil moisture models.

Modeling L-Band Synthetic Aperture Radar Data Through Dielectric Changes in Soil Moisture and Vegetation Over Shrublands

L-band airborne synthetic aperture radar observations were made over California shrublands to better understand the effects of soil and vegetation parameters on backscattering coefficient $(\sigma ^{0})$ . Temporal changes in $\sigma ^{0}$ of up to 3 dB were highly correlated to surface soil moisture but not to vegetation, even though vegetation water content (VWC) varied seasonally by a factor of two. HH was always greater than VV, suggesting the importance of double-bounce scattering by the woody parts. However, the geometric and dielectric properties of the woody parts did not vary significantly over time. Instead the changes in VWC occurred primarily in thin leaves that may not meaningfully influence absorption and scattering. A physically based model for single scattering by discrete elements of plants successfully simulated the magnitude of the temporal variations in HH, VV, and HH/VV with a difference of less than 0.9 dB for both the mean and standard deviation when compared with the airborne data. In order to simulate the observations, the VWC input of the plant to the model was formulated as a function of plant's dielectric property (water fraction) while the plant geometry remains static in time. In comparison, when the VWC input was characterized by the geometry of a growing plant, the model performed poorly in describing the observed patterns in the $\sigma ^{0}$ changes. The modeling results offer explanation of the observation that soil moisture correlated highly with $\sigma ^{0}$ : the dominant mechanisms for HH and VV are double-bounce scattering by trunk, and soil surface scattering, respectively. The time-series inversion of the physical model was able to retrieve soil moisture with the difference of $- {\text{0.037}}\, \text{m}^{3}{/ \text{m}}^{3}$ (mean), ${\text{0.025}}\, \text{m}^{3}{/ \text{m}}^{3}$ (standard deviation), and 0.89 (correlation), which demonstrates the efficacy of the model-based time-series soil moisture retrieval for shrublands.

A quality assessment of the soil water index by the propagation of ASCAT soil moisture error estimates through an exponential filter

ABSTRACTSoil moisture data from remote-sensing measurements are used extensively and error characterization is critical in assessing their utility for specific applications. In hydrological monitoring and forecast applications, an estimate of the root-zone soil moisture is often required, and the exponential filter constitutes a common approach to obtaining a soil water index (SWI), i.e. the profile soil moisture from the remotely-sensed surface states. Advanced Scatterometer (ASCAT) surface soil moisture (SSM) time series include error estimates as ancillary data, however their effects are rarely considered in SWI computations. In this study, we introduced a simple error propagation scheme in parallel to exponential filter computation, aimed at estimating a SWI noise, which implicitly takes into account both errors and availability of the input SSM data actually used for each single SWI estimation. Integrating a control on SWI noise within quality check procedures was analysed by comparing in situ measurements available at different depths, up to 60 cm, for the period 2007–2014 in 10 stations located in Italy. The capacity of ASCAT-derived SWI to capture the same processes observed in the root-zone by in situ sensors, was evaluated by computing standard statistics (the correlation coefficient r and the root mean square difference [RMSD]) for different ‘a priori’ data masking procedures on SSM data, based on several indicators (processing flag, orbit direction, SSM noise) available for the ASCAT product. The effects of using the information contained in SSM noise and in the proposed SWI noise were also investigated. Removing data according to ‘a posteriori’ control on SWI noise rather than a priori masking SSM data, with the exponential filter T value and the percentage of removed data being equal, was found to generally improve the statistics of the resulting SWI time series. In addition, evaluating T on the SWI time series screened according to the SWI noise, outperforms other approaches analysed in almost every test case.

Improving ANN model performance in runoff forecasting by adding soil moisture input and using data preprocessing techniques

AbstractThis study attempts to improve the accuracy of runoff forecasting from two aspects: one is the inclusion of soil moisture time series simulated from the GR4J conceptual rainfall–runoff model as (ANN) input; the other is preprocessing original data series by singular spectrum analysis (SSA). Three watersheds in China were selected as case studies and the ANN1 model only with runoff and rainfall as inputs without data preprocessing was used to be the benchmark. The ANN2 model with soil moisture as an additional input, the SSA-ANN1 and SSA-ANN2 models with the same inputs as ANN1 and ANN2 using data preprocessing were studied. It is revealed that the degree of improvement by SSA is more significant than by the inclusion of soil moisture. Among the four studied models, the SSA-ANN2 model performs the best.

Uncertainty of groundwater recharge estimated from a water and energy balance model

Coupled water and energy balance models have become increasingly popular for estimating groundwater recharge, because of the integration of energy and water balances and sometimes carbon balance. The additional balances are thought to constrain the water balance and as a result should help reduce the uncertainty of groundwater recharge. However, these models usually have a large number of parameters. The uncertainty of these parameters may result in a large uncertainty in groundwater recharge estimates. This study aims to assess the potential uncertainty of groundwater recharge estimated from a widely used water and energy model. It is largely based on annual pasture vegetation in the lower part of the Campaspe catchment in southeast Australia. A Monte Carlo analysis method was employed to examine potential uncertainties introduced by different types of errors. The results show that for a mean rainfall of 398mm/y and using a particular set of pedotransfer functions for deriving soil hydraulic parameters, the estimated recharge ranged from 7 to 144mm/y due to the uncertainty in vegetation parameters. This upper bound of the recharge range increased to 236mm/y when using different sets of pedotransfer functions. Through several synthetic test cases, this study shows that soil moisture time series may not offer much help for reducing recharge uncertainty, whereas evapotranspiration time series are able to reduce recharge uncertainty by more than 50%. The reduction in recharge uncertainty steadily improves as the uncertainty in observations reduces.

A modified version of the SMAR model for estimating root-zone soil moisture from time-series of surface soil moisture

Root-zone soil moisture at the regional scale has always been a missing element of the hydrological cycle. Knowing its value could be a great help in estimating evapotranspiration, erosion, runoff, permeability, irrigation needs, etc. The recently developed Soil Moisture Analytical Relationship (SMAR) can relate the surface soil moisture to the moisture content of deeper layers using a physically-based formulation. Previous studies have proved the effectiveness of SMAR in estimating root-zone soil moisture, yet there is still room for improvement in its application. For example, the soil water loss function (i.e. deep percolation and evapotranspiration), assumed to be a linear function in the SMAR model, may produce approximations in the estimation of water losses in the second soil layer. This problem becomes more critical in soils with finer textures. In this regard, the soil moisture profile data from two research sites (AMMA and SCAN) were investigated. The results showed that after a rainfall event, soil water losses decrease following a power pattern until they reach a minimum steady state. This knowledge was used to modify SMAR. In particular, SMAR was modified (MSMAR) by introducing a non-linear soil water loss function that allowed for improved estimates of root zone soil moisture. Keywords: surface soil moisture, root-zone soil moisture, SMAR, soil water loss function, MSMAR

Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products

Abstract. A considerable number of river basins around the world lack sufficient ground observations of hydro-meteorological data for effective water resources assessment and management. Several approaches can be developed to increase the quality and availability of data in these poorly gauged or ungauged river basins; among them, the use of Earth observations products has recently become promising. Earth observations of various environmental variables can be used potentially to increase knowledge about the hydrological processes in the basin and to improve streamflow model estimates, via assimilation or calibration. The present study aims to calibrate the large-scale hydrological model PCRaster GLOBal Water Balance (PCR-GLOBWB) using satellite-based products of evapotranspiration and soil moisture for the Moroccan Oum er Rbia River basin. Daily simulations at a spatial resolution of 5 × 5 arcmin are performed with varying parameters values for the 32-year period 1979–2010. Five different calibration scenarios are inter-compared: (i) reference scenario using the hydrological model with the standard parameterization, (ii) calibration using in situ-observed discharge time series, (iii) calibration using the Global Land Evaporation Amsterdam Model (GLEAM) actual evapotranspiration time series, (iv) calibration using ESA Climate Change Initiative (CCI) surface soil moisture time series and (v) step-wise calibration using GLEAM actual evapotranspiration and ESA CCI surface soil moisture time series. The impact on discharge estimates of precipitation in comparison with model parameters calibration is investigated using three global precipitation products, including ERA-Interim (EI), WATCH Forcing methodology applied to ERA-Interim reanalysis data (WFDEI) and Multi-Source Weighted-Ensemble Precipitation data by merging gauge, satellite and reanalysis data (MSWEP). Results show that GLEAM evapotranspiration and ESA CCI soil moisture may be used for model calibration resulting in reasonable discharge estimates (NSE values from 0.5 to 0.75), although better model performance is achieved when the model is calibrated with in situ streamflow observations. Independent calibration based on only evapotranspiration or soil moisture observations improves model predictions to a lesser extent. Precipitation input affects discharge estimates more than calibrating model parameters. The use of WFDEI precipitation leads to the lowest model performances. Apart from the in situ discharge calibration scenario, the highest discharge improvement is obtained when EI and MSWEP precipitation products are used in combination with a step-wise calibration approach based on evapotranspiration and soil moisture observations. This study opens up the possibility of using globally available Earth observations and reanalysis products of precipitation, evapotranspiration and soil moisture in large-scale hydrological models to estimate discharge at a river basin scale.

Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China

A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input training data consisted of the X-band dual polarization brightness temperature (TB) and the Ka-band V polarization TB from the Advanced Microwave Scanning Radiometer II (AMSR2), Global Land Satellite product (GLASS) Leaf Area Index (LAI), precipitation from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), and a global 30 arc-second elevation (GTOPO-30). The output training data were generated from fused SM products of the Japan Aerospace Exploration Agency (JAXA) and the Land Surface Parameter Model (LPRM). The reprocessed fused SM from two years (2013 and 2014) was inputted into the NARXnn for training; subsequently, SM during a third year (2015) was estimated. Direct and indirect validations were then performed during the period 2015 by comparing with in situ measurements, SM from JAXA, LPRM and the Global Land Data Assimilation System (GLDAS), as well as precipitation data from TRMM and GPM. The results showed that the SM predictions from NARXnn performed best, as indicated by their higher correlation coefficients (R ≥ 0.85 for the whole year of 2015), lower Bias values (absolute value of Bias ≤ 0.02) and root mean square error values (RMSE ≤ 0.06), and their improved response to precipitation. This method is being used to produce the NARXnn SM product over the HRB in China.

The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products

In this study, the added utility of nonlinear rescaling methods relative to linear methods in the framework of creating a homogenous soil moisture time series has been explored. The performances of 31 linear and nonlinear rescaling methods are evaluated by rescaling the Land Parameter Retrieval Model (LPRM) soil moisture datasets to station-based watershed average datasets obtained over four United States Department of Agriculture (USDA) Agricultural Research Service (ARS) watersheds. The linear methods include first-order linear regression, multiple linear regression, and multivariate adaptive regression splines (MARS), whereas the nonlinear methods include cumulative distribution function matching (CDF), artificial neural networks (ANN), support vector machines (SVM), Genetic Programming (GEN), and copula methods. MARS, GEN, SVM, ANN, and the copula methods are also implemented to utilize lagged observations to rescale the datasets. The results of a total of 31 different methods show that the nonlinear methods improve the correlation and error statistics of the rescaled product compared to the linear methods. In general, the method that yielded the best results using training data improved the validation correlations, on average, by 0.063, whereas ELMAN ANN and GEN, using lagged observations methods, yielded correlation improvements of 0.052 and 0.048, respectively. The lagged observations improved the correlations when they were incorporated into rescaling equations in linear and nonlinear fashions, with the nonlinear methods (particularly SVM and GEN but not ANN and copula) benefitting from these lagged observations more than the linear methods. The overall results show that a large majority of the similarities between the LPRM and watershed average datasets are due to linear relations; however, nonlinear relations clearly exist, and the use of nonlinear rescaling methods clearly improves the accuracy of the rescaled product.

Global characterization of surface soil moisture drydowns

AbstractLoss terms in the land water budget (including drainage, runoff, and evapotranspiration) are encoded in the shape of soil moisture “drydowns”: the soil moisture time series directly following a precipitation event, during which the infiltration input is zero. The rate at which drydowns occur—here characterized by the exponential decay time scale τ—is directly related to the shape of the loss function and is a key characteristic of global weather and climate models. In this study, we use 1 year of surface soil moisture observations from NASA's Soil Moisture Active Passive mission to characterize τ globally. Consistent with physical reasoning, the observations show that τ is lower in regions with sandier soils, and in regions that are more arid. To our knowledge, these are the first global estimates of τ—based on observations alone—at scales relevant to weather and climate models.

Modeling multidecadal surface water inundation dynamics and key drivers on large river basin scale using multiple time series of Earth‐observation and river flow data

AbstractPeriodically inundated floodplain areas are hot spots of biodiversity and provide a broad range of ecosystem services but have suffered alarming declines in recent history. Despite their importance, their long‐term surface water (SW) dynamics and hydroclimatic drivers remain poorly quantified on continental scales. In this study, we used a 26 year time series of Landsat‐derived SW maps in combination with river flow data from 68 gauges and spatial time series of rainfall, evapotranspiration and soil moisture to statistically model SW dynamics as a function of key drivers across Australia's Murray‐Darling Basin (∼1 million km2). We fitted generalized additive models for 18,521 individual modeling units made up of 10 × 10 km grid cells, each split into floodplain, floodplain‐lake, and nonfloodplain area. Average goodness of fit of models was high across floodplains and floodplain‐lakes (r2 > 0.65), which were primarily driven by river flow, and was lower for nonfloodplain areas (r2 > 0.24), which were primarily driven by rainfall. Local climate conditions were more relevant for SW dynamics in the northern compared to the southern basin and had the highest influence in the least regulated and most extended floodplains. We further applied the models of two contrasting floodplain areas to predict SW extents of cloud‐affected time steps in the Landsat series during the large 2010 floods with high validated accuracy (r2 > 0.97). Our framework is applicable to other complex river basins across the world and enables a more detailed quantification of large floods and drivers of SW dynamics compared to existing methods.

Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index

This study presents a back propagation neural network (BPNN) method to rebuild a global and long-term soil moisture (SM) series, adopting the microwave vegetation index (MVI). The data used in our study include Soil Moisture and Ocean Salinity (SMOS) Level 3 soil moisture (SMOSL3sm) data, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) Level 3 brightness temperature (TB) data and L3 SM products. The BPNNs on each grid were trained over July 2010–June 2011, and the entire year of 2013, with SMOSL3sm as a training target, and taking the reflectivities (Rs) of the C/X/Ku/Ka/Q bands, and the MVI from AMSR-E/AMSR2 TB data, as input, in which the MVI is used to correct for vegetation effects. The training accuracy of networks was evaluated by comparing soil moisture products produced using BPNNs (NNsm hereafter) with SMOSL3sm during the BPNN training period, in terms of correlation coefficient (CC), bias (Bias), and the root mean square error (RMSE). Good global results were obtained with CC = 0.67, RMSE = 0.055 m3/m3 and Bias = −0.0005 m3/m3, particularly over Australia, Central USA, and Central Asia. With these trained networks over each pixel, a global and long-term soil moisture time series, i.e., 2003–2015, was built using AMSR-E TB from 2003 to 2011 and AMSR2 TB from 2012 to 2015. Then, NNsm products were evaluated against in situ SM observations from all SCAN (Soil Climate Analysis Network) sites (SCANsm). The results show that NNsm has a good agreement with in situ data, and can capture the temporal dynamics of in situ SM, with CC = 0.52, RMSE = 0.084 m3/m3 and Bias = −0.002 m3/m3. We also evaluate the accuracy of NNsm by comparing with AMSR-E/AMSR2 SM products, with results of a regression method. As a conclusion, this study provides a promising BPNN method adopting MVI to rebuild a long-term SM time series, and this could provide useful insights for the future Water Cycle Observation Mission (WCOM).

Full-featured ground vehicle mobility analysis using different soil moisture sources

We investigated the influence of using three different soil moisture sources WindSat (a satellite), land information system (LIS) (a computer model), and in situ ground sensors in a full-featured ground vehicle mobility analysis model. Soil moisture, soil type, land use, and slope were used to estimate the mobility in dry and wet scenarios for six vehicles using the NATO Reference Mobility Model (NRMM). The dry scenario showed nearly the same results for all three soil moisture sources. In the wet scenario, modelled travel times based on LIS were about 26% faster compared to those from the in situ moisture. Modelled times based on WindSat averaged 11% slower than those from the in situ moisture. For the test case we found agreement between the results of the current full-feature model against a previously-developed model using only soil moisture time series. This demonstrates that soil moisture can be important even as one terrain factor among many.

Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data

A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer—Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and with a similar dynamical range to that of the SMOS SM dataset. This method consists of training a neural network (NN) to obtain a global non-linear relationship linking AMSR-E brightness temperatures ( T b ) to the SMOS L3 SM dataset on the concurrent mission period of 1.5 years. Then, the NN model is used to derive soil moisture from past AMSR-E observations. It is shown that in spite of the different frequencies and sensing depths of AMSR-E and SMOS, it is possible to find such a global relationship. The sensitivity of AMSR-E T b ’s to soil temperature ( T s o i l ) was also evaluated using European Centre for Medium-Range Weather Forecast Interim/Land re-analysis (ERA-Land) and Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) model data. The best combination of AMSR-E T b ’s to retrieve T s o i l is H polarization at 23 and 36 GHz plus V polarization at 36 GHz. Regarding SM, several combinations of input data show a similar performance in retrieving SM. One NN that uses C and X bands and T s o i l information was chosen to obtain SM in the 2003–2011 period. The new dataset shows a low bias (<0.02 m3/m3) and low standard deviation of the difference (<0.04 m3/m3) with respect to SMOS L3 SM over most of the globe’s surface. The new dataset was evaluated together with other AMSR-E SM datasets and the Climate Change Initiative (CCI) SM dataset against the MERRA-Land and ERA-Land models for the 2003–2011 period. All datasets show a significant bias with respect to models for boreal regions and high correlations over regions other than the tropical and boreal forest. All of the global SM datasets including AMSR-E NN were also evaluated against a large number of in situ measurements over four continents. Over Australia, all datasets show a strong level of agreement with in situ measurements. Models perform better over Europe and mountainous regions in North America. Remote sensing datasets (in particular NN and the Land Parameter Retrieval Model (LPRM)) perform as well as models for other North American sites and perform better than models over the Sahel region.

Combined soil-terrain stratification for characterizing catchment-scale soil moisture variation

Soil properties and terrain characteristics influence spatiotemporal patterns of soil moisture across a watershed. To improve the predictive power of landscape hydrologic models, it is essential to consider both soil and terrain attributes when stratifying a catchment into similar hydrologic functional units. In this study, we developed and validated a new catchment-scale stratification scheme for the Shale Hills watershed by combining soil and terrain attributes in an attempt to delineate soil-landscape units with similar soil moisture dynamics. Terrain was combined with soils information by first using a Random Forest supervised classification algorithm to predict a detailed soil map using 47 field soil samples and terrain variables derived from 1-m LiDAR. A slope class map generated from the LiDAR-derived digital elevation model (DEM) was overlaid on the predicted soil map to delineate areas of similar slope value across the catchment. We compared the performance of this new stratification scheme with two classical stratification schemes, a soil map developed from detailed field survey and a landform unit map based on the DEM, for estimating soil moisture time-series across the forested watershed. The combined soil-terrain method outperformed classical stratification schemes in estimating soil moisture time-series over a 4-year period. Our results demonstrate that combining soil and terrain attributes can help improve the stratification of a catchment into similar soil hydrologic functional units, which is valuable to distributed hydrology modeling and other applications.

Data-model comparison of temporal variability in long-term time series of large-scale soil moisture

Soil moisture is at the heart of many processes connected to water cycle, climate, ecosystem and societal conditions. This paper investigates the ability of a relatively simple analytical soil-mois ...

The Impact of Vertical Measurement Depth on the Information Content of Soil Moisture for Latent Heat Flux Estimation

Abstract This study aims to identify the impact of vertical support on the information content of soil moisture (SM) for latent heat flux estimation. This objective is achieved via calculation of the mutual information (MI) content between multiple soil moisture variables (with different vertical supports) and current/future evaporative fraction (EF) using ground-based soil moisture and latent/sensible heat flux observations acquired from the AmeriFlux network within the contiguous United States. Through the intercomparison of MI results from different SM–EF pairs, the general value (for latent heat flux estimation) of superficial soil moisture observations , vertically integrated soil moisture observations , and vertically extrapolated soil moisture time series [soil wetness index (SWI) from a simple low-pass transformation of ] are examined. Results suggest that, contrary to expectations, 2-day averages of and have comparable mutual information with regards to EF. That is, there is no clear evidence that the information content for flux estimation is enhanced via deepening the vertical support of superficial soil moisture observations. In addition, the utility of SWI in monitoring and forecasting EF is partially dependent on the adopted parameterization of time-scale parameter T in the exponential filter. Similar results are obtained when analyses are conducted at the monthly time scale, only with larger error bars. The contrast between the results of this paper and past work focusing on utilizing soil moisture to predict vegetation condition demonstrates that the particular application should be considered when characterizing the information content of soil moisture time series measurements.