Estimating soil moisture using remote sensing data: A machine learning approach

Soil moisture is an integral quantity in hydrology that represents the average conditions in a finite volume of soil. In this paper, a novel regression technique called Support Vector Machine (SVM) is presented and applied to soil moisture estimation using remote sensing data. SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach. SVM has been used to predict a quantity forward in time based on training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. SVM model is applied to 10 sites for soil moisture estimation in the Lower Colorado River Basin (LCRB) in the western United States. The sites comprise low to dense vegetation. Remote sensing data that includes backscatter and incidence angle from Tropical Rainfall Measuring Mission (TRMM), and Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) are used to estimate soil water content (SM). Simulated SM (%) time series for the study sites are available from the Variable Infiltration Capacity Three Layer (VIC) model for top 10 cm layer of soil for the years 1998–2005. SVM model is trained on 5 years of data, i.e. 1998–2002 and tested on 3 years of data, i.e. 2003–2005. Two models are developed to evaluate the strength of SVM modeling in estimating soil moisture. In model I, training and testing are done on six sites, this results in six separate SVM models – one for each site. Model II comprises of two subparts: (a) data from all six sites used in model I is combined and a single SVM model is developed and tested on same sites and (b) a single model is developed using data from six sites (same as model II-A) but this model is tested on four separate sites not used to train the model. Model I shows satisfactory results, and the SM estimates are in good agreement with the estimates from VIC model. The SM estimate correlation coefficients range from 0.34 to 0.77 with RMSE less than 2% at all the selected sites. A probabilistic absolute error between the VIC SM and modeled SM is computed for all models. For model I, the results indicate that 80% of the SM estimates have an absolute error of less than 5%, whereas for model II-A and II-B, 80% and 60% of the SM estimates have an error less than 10% and 15%, respectively. SVM model is also trained and tested for measured soil moisture in the LCRB. Results with RMSE, MAE and R of 2.01, 1.97, and 0.57, respectively show that the SVM model is able to capture the variability in measured soil moisture. Results from the SVM modeling are compared with the estimates obtained from feed forward-back propagation Artificial Neural Network model (ANN) and Multivariate Linear Regression model (MLR); and show that SVM model performs better for soil moisture estimation than ANN and MLR models.     

Spatial explicit soil moisture analysis: pattern and its stability at small catchment scale in the loess hilly region of China

Soil moisture is essential for plant growth and terrestrial ecosystems, especially in arid and semi‐arid regions. This study aims to quantify the variation of soil moisture content and its spatial pattern as well as the influencing factors. The experiment is conducted in a small catchment named Yangjuangou in the loess hilly region of China. Soil moisture to a depth of 1 m has been obtained by in situ sampling at 149 sites with different vegetation types before and after the rainy season. Elevation, slope position, slope aspect, slope gradient and vegetation properties are investigated synchronously. With the rainy season coming, soil moisture content increases and then reaches the highest value after the rainy season. Fluctuation range and standard deviation of soil moisture decrease after a 4‐month rainy season. Standard deviation of soil moisture increases with depth before the rainy season; after the rainy season, it decreases within the 0‐ to 40‐cm soil depth but then increases with depths below 40 cm. The stability of the soil moisture pattern at the small catchment scale increases with depth. The geographical position determines the framework of soil moisture pattern. Soil moisture content with different land‐use types is significantly increased after the rainy season, but the variances of land‐use types are significantly different. Landform and land‐use types can explain most of the soil moisture spatial variations. Soil moisture at all sample sites increases after the rainy season, but the spatial patterns of soil moisture are not significantly changed and display temporal stability despite the influence of the rainy season. Copyright © 2013 John Wiley & Sons, Ltd.     

Scaling characteristics of spatial patterns of soil moisture from distributed modelling

Characterizing the spatial dynamics of soil moisture fields is a key issue in hydrology, offering an avenue to improve our understanding of complex land surface–atmosphere interactions. In this paper, the statistical structure of soil moisture patterns is examined using modelled soil moisture obtained from the North American Land Data Assimilation System (NLDAS) at 0.125° resolution. The study focuses on the vertically averaged soil moisture in the top 10 cm and 100 cm layers. The two variables display a weak dependence for lower values of surface soil moisture, with the strength of the relationship increasing with the water content of the top layer. In both cases, the variance of the soil moisture follows a power law decay as a function of the averaging area. The superficial layer shows a lower degree of spatial organization and higher temporal variability, which is reflected in rapid changes in time of the slope of the scaling functions of the soil moisture variance. Conversely, the soil moisture in the top 100 cm has lower variability in time and larger spatial correlation. The scaling of these patterns was found to be controlled by the changes in the soil water content. Results have implications for the downscaling of soil moisture to prevent model bias.     

Soil water content in southern England derived from a cosmic‐ray soil moisture observing system – COSMOS‐UK

Cosmic‐ray soil moisture sensors have the advantage of a large measurement footprint (approximately 700 m in diameter) and are able to operate continuously to provide area‐averaged near‐surface (top 10–20 cm) volumetric soil moisture content at the field scale. This paper presents the application of this technique at four sites in southern England over almost 3 years. Results show the soil moisture response to contrasting climatic conditions during 2011–2014 and are the first such field‐scale measurements made in the UK. These four sites are prototype stations for a UK COsmic‐ray Soil Moisture Observing System, and particular consideration is given to sensor operating conditions in the UK. Comparison of these soil water content observations with the Joint UK Land Environment Simulator 10‐cm soil moisture layer shows that these data can be used to test and diagnose model performance and indicate the potential for assimilation of these data into hydro‐meteorological models. The application of these large‐area soil water content measurements to evaluate remotely sensed soil moisture products is also demonstrated. Numerous applications and the future development of a national COsmic‐ray Soil Moisture Observing System network are discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

The NAFE’06 data set: Towards soil moisture retrieval at intermediate resolution

The National Airborne Field Experiment 2006 (NAFE’06) was conducted during a three week period of November 2006 in the Murrumbidgee River catchment, located in southeastern Australia. One objective of NAFE’06 was to explore the suitability of the area for SMOS (Soil Moisture and Ocean Salinity) calibration/validation and develop downscaling and assimilation techniques for when SMOS does come on line. Airborne L-band brightness temperature was mapped at 1 km resolution 11 times (every 1–3 days) over a 40 by 55 km area in the Yanco region and 3 times over a 40 by 50 km area that includes Kyeamba Creek catchment. Moreover, multi-resolution, multi-angle and multi-spectral airborne data including surface temperature, surface reflectance (green, read and near infrared), lidar data and aerial photos were acquired over selected areas to develop downscaling algorithms and test multi-angle and multi-spectral retrieval approaches. The near-surface soil moisture was measured extensively on the ground in eight sampling areas concurrently with aircraft flights, and the soil moisture profile was continuously monitored at 41 sites. Preliminary analyses indicate that (i) the uncertainty of a single ground measurement was typically less than 5% vol. (ii) the spatial variability of ground measurements at 1 km resolution was up to 10% vol. and (iii) the validation of 1 km resolution L-band data is facilitated by selecting pixels with a spatial soil moisture variability lower than the point-scale uncertainty. The sensitivity of passive microwave and thermal data is also compared at 1 km resolution to illustrate the multi-spectral synergy for soil moisture monitoring at improved accuracy and resolution. The data described in this paper are available at www.nafe.unimelb.edu.au.     

Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation

With well-determined hydraulic parameters in a hydrologic model, a traditional data assimilation method (such as the Kalman filter and its extensions) can be used to retrieve root zone soil moisture under uncertain initial state variables (e.g., initial soil moisture content) and good simulated results can be achieved. However, when the key soil hydraulic parameters are incorrect, the error is non-Gaussian, as the Kalman filter will produce a persistent bias in its predictions. In this paper, we propose a method coupling optimal parameters and extended Kalman filter data assimilation (OP-EKF) by combining optimal parameter estimation, the extended Kalman filter (EKF) assimilation method, a particle swarm optimization (PSO) algorithm, and Richards’ equation. We examine the accuracy of estimating root zone soil moisture through the optimal parameters and extended Kalman filter data assimilation method by using observed in situ data at the Meiling experimental station, China. Results indicate that merely using EKF for assimilating surface soil moisture content to obtain soil moisture content in the root zone will produce a persistent bias between simulated and observed values. Using the OP-EKF assimilation method, estimates were clearly improved. If the soil profile is heterogeneous, soil moisture retrieval is accurate in the 0-50 cm soil profile and is inaccurate at 100 cm depth. Results indicate that the method is useful for retrieving root zone soil moisture over large areas and long timescales even when available soil moisture data are limited to the surface layer, and soil moisture content are uncertain and soil hydraulic parameters are incorrect.     

Long‐term effects of revegetation on soil hydrological processes in vegetation‐stabilized desert ecosystems

Planting of sand‐binding vegetation in the Shapotou region on the southeastern edge of the Tengger Desert began in 1956. The revegetation programme successfully stabilized formerly mobile dunes in northern China, permitting the operation of the Baotou‐Lanzhou railway. Long‐term monitoring has shown that the revegetation programme produced various ecological changes, including the formation of biological soil crusts (BSCs). To gain insight into the role of BSCs in both past ecological change and current ecological evolution at the revegetation sites, we used field measurements and HYDRUS‐1D model simulations to investigate the effects of BSCs on soil hydrological processes at revegetated sites planted in 1956 and 1964 and at an unplanted mobile dune site. The results demonstrate that the formation of BSCs has altered patterns of soil water storage, increasing the moisture content near the surface (0–5 cm) while decreasing the moisture content in deeper layers (5–120 cm). Soil evaporation at BSC sites is elevated relative to unplanted sites during periods when canopy coverage is low. Rainfall infiltration was not affected by BSCs during the very dry period that was studied (30 April to 30 September 2005); during periods with higher rainfall intensity, differences in infiltration may be expected due to runoff at BSC sites. The simulated changes in soil moisture storage and hydrological processes are consistent with ongoing plant community succession at the revegetated sites, from deep‐rooted shrubs to more shallow‐rooted herbaceous species. Copyright © 2009 John Wiley & Sons, Ltd.     

Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals

The upcoming deployment of satellite-based microwave sensors designed specifically to retrieve surface soil moisture represents an important milestone in efforts to develop hydrologic applications for remote sensing observations. However, typical measurement depths of microwave-based soil moisture retrievals are generally considered too shallow (top 2–5 cm of the soil column) for many important water cycle and agricultural applications. Recent work has demonstrated that thermal remote sensing estimates of surface radiometric temperature provide a complementary source of land surface information that can be used to define a robust proxy for root-zone (top 1 m of the soil column) soil moisture availability. In this analysis, we examine the potential benefits of simultaneously assimilating both microwave-based surface soil moisture retrievals and thermal infrared-based root-zone soil moisture estimates into a soil water balance model using a series of synthetic twin data assimilation experiments conducted at the USDA Optimizing Production Inputs for Economic and Environmental Enhancements (OPE³) site. Results from these experiments illustrate that, relative to a baseline case of assimilating only surface soil moisture retrievals, the assimilation of both root- and surface-zone soil moisture estimates reduces the root-mean-square difference between estimated and true root-zone soil moisture by 50% to 35% (assuming instantaneous root-zone soil moisture retrievals are obtained at an accuracy of between 0.020 and 0.030 m³ m⁻³). Most significantly, improvements in root-zone soil moisture accuracy are seen even for cases in which root-zone soil moisture retrievals are assumed to be relatively inaccurate (i.e. retrievals errors of up to 0.070 m³ m⁻³) or limited to only very sparse sampling (i.e. one instantaneous measurement every eight days). Preliminary real data results demonstrate a clear increase in the R² correlation coefficient with ground-based root-zone observations (from 0.51 to 0.73) upon assimilation of actual surface soil moisture and tower-based thermal infrared temperature observations made at the OPE³ study site.     

Soil moisture dynamics in coastal savanna soils in the tropics under different soil management practices

Abstract

Soil moisture is important for crop cultivation and its adequacy to meet crop-water requirements is determined by the degree of soil management practised and the quantity of water applied to the soil. This study investigates soil moisture dynamics on three plots: Bare (clean, weeds removed), Weedy (kept weedy), and Mulched (cleared of weeds and fully covered with grass mulch) during rainy and dry periods at the Teaching and Research Farm at the University of Cape Coast, in the coastal savanna zone of Ghana. Soil moisture dynamics under different levels of soil compaction were also studied. A Massey Ferguson tractor (MF265) was used to compact the soil at various levels by making 0, 1, 5, 9 and 13 passes. During both the rainy and the dry periods, moisture retention in the soil under bare, weedy and mulched plots increased with depth. During the rainy period, the mean soil moisture retention was in the order: Mulched > Weedy > Bare at both 0–20 cm and 20–40 cm depths. Within a 7–day period, soil moisture measurements from a day after heavy rainfall (intensity > 7 mm h^?1) gave mean moisture losses of 2.7, 4.1 and 3.9% for the Bare, Weedy and Mulched plots, respectively. During the dry period, however, the mean soil moisture retention was of the order: Mulched > Bare > Weedy at both 0–20 cm and 20–40 cm depths. Mean moisture loss during a 7–day dry period was 4.5, 2.9 and 3.4% for the Bare, Weedy and Mulched plots, respectively. Under different levels of soil compaction, the mean moisture retention in the soil increased from 8.3% at 0 pass to 17.8% at 13 passes within the 0–20 cm depth, whilst it decreased from 13.3 to 5.9% from 0 to 13 passes, respectively, within the 20–40 cm depth. It was realized that at less than two passes, the mean soil moisture retention within the 0–20 cm depth was less than the mean moisture retention within the 20–40 cm depth, but the converse happened for more than two passes. The study showed that mulching the soil surface helped to retain enough soil moisture during both the rainy and the dry seasons. Also, soil with high sand content required some sort of soil compaction in order to retain enough moisture at the crop rooting zone.     

The effects of scene heterogeneity on soil moisture retrieval from passive microwave data

The τ–ω model of microwave emission from soil and vegetation layers is widely used to estimate soil moisture content from passive microwave observations. Its application to prospective satellite-based observations aggregating several thousand square kilometres requires understanding of the effects of scene heterogeneity. The effects of heterogeneity in soil surface roughness, soil moisture, water area and vegetation density on the retrieval of soil moisture from simulated single- and multi-angle observing systems were tested. Uncertainty in water area proved the most serious problem for both systems, causing errors of a few percent in soil moisture retrieval. Single-angle retrieval was largely unaffected by the other factors studied here. Multiple-angle retrievals errors around one percent arose from heterogeneity in either soil roughness or soil moisture. Errors of a few percent were caused by vegetation heterogeneity. A simple extension of the model vegetation representation was shown to reduce this error substantially for scenes containing a range of vegetation types.     

Vertical variations and transport mechanism of soil moisture in response to vegetation restoration on the Loess Plateau of China

Soil moisture is essential for vegetation restoration in arid and semi-arid regions. Ascertaining the vertical distribution and transportation of soil moisture under different vegetation types has a profound effect on the ecological construction. In this study, the soil moisture at a depth of 500 cm for four typical vegetation types, including Robinia pseudoacacia, Caragana korshinskii, Stipa bungeana, and corn, were investigated and compared in the Zhifanggou watershed of the Loess plateau. Additionally, hydrogen and oxygen stable isotopes were detected to identify the transport mechanism of soil moisture. The results showed vertical distribution and transportation of soil moisture were different under different vegetation types. Depth-averaged soil moisture under S. bungeana and corn generally increased along the profile, while C. korshinskii and R. pseudoacacia showed weakly increasing and relatively stable after an obvious decreasing trend (0–40 cm). The soil moisture under R. pseudoacacia was lower than that under other vegetation types, especially in deep layer. However, the effect of R. pseudoacacia on soil moisture in the topsoil (< 30 cm) could be positive. For R. pseudoacacia (160–500 cm), C. korshinskii (0–500 cm), and S. bungeana (0–100 cm), the soil moisture declined with increased in vegetation age. Planting arbor species such as R. pseudoacacia intensified the decline of soil moisture on the Loess Plateau. The capacity of evaporation fractionation of soil moisture followed the sequence: corn > S. bungeana > R. pseudoacacia > C. korshinskii. The δ¹⁸O values in soil water fluctuated across the profile. The δ¹⁸O values changed sharply in upper layer and generally remained stable in deep layer. However, in middle layer, the vertical distribution characteristics of the δ¹⁸O values were different under different vegetation types. We estimated that piston flow was the main mode of precipitation infiltration, and the occurrence of preferential flow was related to vegetation types. These results were helpful to improve the understanding of the response of deep soil moisture to vegetation restoration and inform practices for sustainable water management.     

A case study of spatial heterogeneity of soil moisture in the Loess Plateau, western China： A geostatistical approach

Soil moisture distribution shows highly variation both spatially and temporally. This study assesses the spatial heterogeneity of soil moisture on a hill-slope scale in the Loess Plateau in West China by using a geostatistical approach. Soil moisture was measured by time-domain reflectometry （TDR） in 313 samples. Two kinds of sampling scales were used （2 × 2 m and 20 ×20 m） at two soil layers （0-30 cm and 30-60 cm）. The general characteristics of soil moisture were analyzed by a classical statistics method, and the spatial heterogeneity of soil moisture was analyzed using a geostatistical approach. The results showed that the spherical model is the best-fit model to simulate soil moisture on the experimental hill-slope. The parameters of this model indicated that the spatial dependence of soil moisture in the selected hill-slope was moderate. Even the 2 × 2 m sampling scale was too coarse to show the detailed spatial variances of soil moisture in this area. The dependent distance increased from 27.4 m to 494.16 m as the sampling scale became coarse （from 2× 2 m to 20 ×20 m）. A map of soil moisture was generated by using original soil moisture data and interpolated values determined by the Kriging method. The average soil moisture （area weighted） in the different layers of soil was calculated on the basis of this map （10.94% for the 0-30 cm soil layer, 11.88% for the 30-60 cm soil layer）. This average soil moisture is lower than the corresponding average effective soil moisture, which suggests that the soil moisture is not sufficient to support vegetation in this area.     

The impact of natural vegetation restoration on surface soil moisture of secondary forests and shrubs in the karst region of southwest China

Soil moisture is crucial to vegetation restoration in karst areas, and climate factors and vegetation restoration are key factors affecting changes in soil moisture. However, there is still much controversy over the long-term changes in soil moisture during vegetation restoration. In order to reveal the changes in soil moisture during vegetation restoration, we conducted long-term positioning monitoring of soil moisture at 0–10 and 10–20 cm on secondary forests sample plot (SF, tree land) and shrubs sample plot (SH, shrub land) in karst areas from 2013 to 2020. The results showed that the aboveground biomass of SF and SH increased by 50% and 240%, respectively, and the soil moisture of the SF and SH showed an increasing trend. When shrubs are restored to trees in karst areas, the soil moisture becomes more stable. However, the correlation coefficients (R²) between the annual rainfall and the annual average soil moisture of SF and SH are 0.84 and 0.55, respectively, indicating that soil moistures in tree land are more affected by rainfall. The soil moisture of shrubs and trees are relatively low during the months of alternating rainy and dry seasons. Rainfall has a very significant impact on the soil moisture of tree land, while air temperature and wind speed have a significant impact on the soil moisture of tree land, but the soil moistures of shrub land are very significantly affected by rainfall and relative humidity. Therefore, during the process of vegetation restoration from shrubs to trees, the main meteorological factors that affect soil moisture changes will change. The results are important for understanding the hydrological processes in the ecological restoration process of different vegetation types in karst areas.     

Vegetation structure controls on snow and soil moisture in restored ponderosa pine forests

Thinning of semi-arid forests to reduce wildfire risk is believed to improve forest health by increasing soil moisture. Increased snowpack, reduced transpiration and reduced rainfall interception are frequently cited mechanisms by which reduced canopy density may increase soil moisture. However, the relative importance of these factors has not been rigorously evaluated in field studies. We measured snow depth, snow water equivalent (SWE) and the spatial and temporal variation in soil moisture at four experimental paired treatment-control thinning sites in high elevation ponderosa pine forest northern Arizona, USA. We compared snow and soil moisture measurements with forest structure metrics derived from aerial imagery and 3-dimensional lidar data to determine the relationship between vegetation structure, snow and soil moisture throughout the annual hydrologic cycle. Soil moisture was consistently and significantly higher in thinned forest plots, even though the treatments were performed 8–11 years before this study. However, we did not find evidence that SWE was higher in thinned forests across a range of snow conditions. Regression tree analysis of soil moisture and vegetation structure data provided some evidence that localized differences in transpiration and interception of precipitation influence the spatial pattern of soil moisture at points in the annual hydrologic cycle when the system is becoming increasingly water limited. However, vegetation structure explained a relatively low amount of the spatial variance (R² < 0.23) in soil moisture. Continuous measurements of soil moisture in depth profiles showed stronger attenuation of soil moisture peaks in thinned sites, suggesting differences in infiltration dynamics may explain the difference in soil moisture between treatments as opposed to overlying vegetation alone. Our results show limited support for commonly cited relationships between vegetation structure, snow and soil moisture and indicate that future research is needed to understand how reduction in tree density alters soil hydraulic properties.     

Soil moisture–temperature relationships: results from two field experiments

This paper analyses data from two field experiments in Chickasha, Oklahoma, and Tifton, Georgia, carried out in July 1999 and June 2000 respectively. The observations on soil moisture at two depths, viz. 0–2·5 and 0–5·0 cm, surface temperature, and temperatures at 1, 5 and 10 cm depths are analysed. The relationship between the soil moisture and the temperature variability in time is examined as a function of vegetation type and location. Results from these experiments show that, during drydown, surface temperature shows an increase that corresponds to a decrease in the soil moisture. Linear models for prediction of soil moisture (at both depths) using surface temperature observations are examined. Copyright © 2003 John Wiley & Sons, Ltd.     

Comparison of varied complexity models simulating recharge at the field scale

Numerical models are frequently used for the regional quantification of groundwater recharge. However there is a wide range of potential models available that represent the land surface with varying degrees of complexity, but which are rarely tested against observations at the field scale. We compared four models that simulate potential recharge at four intensively monitored sites with different vegetation and soil types in two adjacent catchments. These models were: Penman–Grindley, UN Food and Agricultural Organization, SPAtial Distributed Evaporation and Joint UK Land Environment Simulator. Standardized, unoptimized land surface datasets and pertinent literature were used for parameterization to reflect practice in regional water resource management and planning in the UK. The models were validated against soil moisture observations at all sites, as well as observed transpiration and interception and calculated total evaporation over a year at a woodland site. Soil moisture observations were generally reproduced well, but there were significant differences in how the models apportioned precipitation through the hydrological cycle. This demonstrates that soil moisture data alone are not a good diagnostic for groundwater recharge models. Significant differences in potential recharge were produced by models at both grassland sites, although simulated average annual potential recharge varied by only 15% at the grassland site on permeable soil. At the woodland sites, soil moisture contents were reproduced least accurately, and there were large differences in potential recharge at both woodland sites. This predominantly resulted from varied and inaccurate simulation of evaporation, particularly in the form of interception losses where this was explicitly represented in models. Differences in model structure, such as runoff representation, and parameter selection also influenced all results. Hydrological Processes © 2013 John Wiley & Sons, Ltd.     

Simulating daily, monthly and annual water balances in a land surface model using alternative root water uptake schemes

Hydrological simulations at multi-temporal time scales by a widely used land surface model (LSM) are investigated under contrasting vegetation and meteorological conditions. Our investigation focuses particularly on the effects of two different representations of root water uptake and root profile on simulated evapotranspiration (ET) and soil moisture by the Integrated BIosphere Simulator (IBIS). For this purpose, multi-year eddy covariance measurements, collected at four flux-tower sites across North America, were used to gauge IBIS simulations with: (a) its standard version (IBIS2.1), in which static root water uptake (RWU) and root profile schemes are incorporated; and (b) a modified version in which dynamic RWU and root profile schemes replaces the static schemes used in the standard version. Overall, our results suggest that the modified version of the model performs more realistically than the standard version, particularly when high atmospheric demand for evaporation is combined with high atmospheric vapour pressure deficit and low soil water availability. The overall correlation between simulated and measured monthly ET rates at the simulated sites reached 0.87 and 0.91 for the standard and the modified versions, respectively. Our results also show that the incorporation of the dynamic RWU in IBIS yields improved simulations of ET under very dry conditions, when soil moisture falls down to very low levels. This suggests that adequate representations of vegetation responses to drought are needed in LSMs as many state of the art climate models projections of future climate indicate more frequent and/or more intense drought events occurring in some regions of the globe. Our analysis also highlighted the urgent need for adequate methodologies to correct field measurements that exhibit energy imbalances in order to provide rigorous assessments of land surface model simulations of heat and mass exchanges between the land surface and the atmosphere.     

Spatial prediction of temporal soil moisture dynamics using HYDRUS‐1D

This article investigates the soil moisture dynamics within two catchments (Stanley and Krui) in the Goulburn River in NSW during a 3‐year period (2005–2007) using the HYDRUS‐1D soil water model. Sensitivity analyses indicated that soil type, and leaf area index were the key parameters affecting model performance. The model was satisfactorily calibrated on the Stanley microcatchment sites with a single point rainfall record from this microcatchment for both surface 30 cm and full‐profile soil moisture measurements. Good correlations were obtained between observed and simulated soil water storage when calibrations for one site were applied to the other sites. We extended the predictions of soil moisture to a larger spatial scale using the calibrated soil and vegetation parameters to the sites in the Krui catchment where soil moisture measurement sites were up to 30 km distant from Stanley. Similarly good results show that it is possible to use a calibrated soil moisture model with measurements at a single site to extrapolate the soil moisture to other sites for a catchment with an area of up to 1000 km² given similar soils and vegetation and local rainfall data. Site predictions were effectively improved by our simple data assimilation method using only a few sample data collected from the site. This article demonstrates the potential usefulness of continuous time, point‐scale soil moisture data (typical of that measured by permanently installed TDR probes) and simulations for predicting the soil wetness status over a catchment of significant size (up to 1000 km²). Copyright © 2012 John Wiley & Sons, Ltd.     

Factors affecting soil moisture spatial variability for a humid forest hillslope

Soil moisture is an important variable in explaining hydrological processes at hillslope scale. The distribution of soil moisture along a hillslope is related to the spatial distribution of the soil properties, the topography, the soil depth, and the vegetation. In order to investigate the factors affecting soil moisture, various environmental data were collected from a humid forest hillslope in this study. Several factors (the wetness index; the contributing area; the local slope; the soil depth; the composition of sand, silt, and clay; the scaling parameter; the hydraulic conductivity; the tree diameter at breast height; and the total weighted basal area) were evaluated for their effect on soil moisture and its distribution over the hillslope at depths of 10, 30, and 60 cm. Both linear correlation analysis and empirical orthogonal function analysis indicated that the soil texture was a dominant factor in soil moisture distribution. The impact of soil hydraulic conductivity was important for all soil moisture ranges at a depth of 30 cm, but those at 10 and 60 cm were limited to very wet and dry conditions, respectively. The relationships of the various factors with the spatial variability of soil moisture indicated the existence of a threshold soil moisture that is related to the composition of the soil and the factors related to the distribution of water in the study area.     

Validation of ERS scatterometer‐derived soil moisture data in the central part of the Duero Basin,Spain

The objective of this study was to validate the soil moisture data derived from coarse‐resolution active microwave data (50 km) from the ERS scatterometer. The retrieval technique is based on a change detection method coupled with a data‐based modelling approach to account for seasonal vegetation dynamics. The technique is able to derive information about the soil moisture content corresponding to the degree of saturation of the topmost soil layer (∼5 cm). To estimate profile soil moisture contents down to 100 cm depth from the scatterometer data, a simple two‐layer water balance model is used, which generates a red noise‐like soil moisture spectrum. The retrieval technique had been successfully applied in the Ukraine in a previous study. In this paper, the performance of the model in a semi‐arid Mediterranean environment characterized by low annual precipitation (400 mm), hot dry summers and sandy soils is investigated. To this end, field measurements from the REMEDHUS soil moisture station network in the semi‐arid parts of the Duero Basin (Spain) were used. The results reveal a significant coefficient of determination (R² = 0·75) for the averaged 0–100 cm soil moisture profile and a root mean square error (RMSE) of 2·2 vol%. The spatial arrangement of the REMEDHUS soil moisture stations also allowed us to study the influence of the small‐scale variability of soil moisture within the ERS scatterometer footprint. The results show that the small‐scale variability in the study area is modest and can be explained in terms of texture fraction distribution in the soil profiles. Copyright © 2004 John Wiley & Sons, Ltd.