Variability Of Surface Soil Moisture Research Articles

Soil Moisture Analyses at ECMWF: Evaluation Using Global Ground-Based In Situ Observations

Abstract In situ soil moisture from 117 stations across the world and under different biome and climate conditions are used to evaluate two soil moisture products from the European Centre for Medium-Range Weather Forecasts (ECMWF)—namely, the operational analysis and the interim reanalysis [ECMWF Re-Analysis Interim (ERA-Interim)]. ECMWF’s operational Integrated Forecasting System (IFS) is based on a continuous effort to improve the analysis and modeling systems, resulting in frequent updates (a few times a year). The ERA-Interim reanalysis is produced by a fixed IFS version (for the main component of the atmospheric model and data assimilation). It has the advantage of being consistent over the whole period from 1979 onward and by design, reanalysis products are more suitable than their operational counterparts for use in climate studies. Although the two analyses show good skills in capturing surface soil moisture variability, they tend to overestimate soil moisture, particularly for dry land. Over the 2008–10 period, averaged statistical scores (correlation, bias, and root-mean-square difference) are 0.70, −0.081 m3 m−3, and 0.113 m3 m−3 for the operational product and 0.63, −0.079 m3 m−3, and 0.121 m3 m−3 for ERA-Interim. Compared to the scheme used in ERA-Interim, the current model used in the IFS has an improved match to soil moisture that is attributed to recent changes in the IFS. Indeed, major upgrades recently implemented in the operational land surface analysis and modeling system improve the surface and the root-zone soil moisture analyses.

RETRIEVING SURFACE SOIL MOISTURE FROM MODIS AND AMSR-E DATA: A CASE STUDY IN TAIWAN

Abstract. Soil moisture is a key factor that controls the exchange of water between land surface evaporation and plant transpiration. Information on surface soil moisture variations in both time and spatial domains is important for numerous applications, especially agricultural and environmental monitoring. This study aimed at retrieving surface soil moisture from daily MODIS and AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) data. A case study was conducted in Taiwan for 2009. Data were processed using the Temperature Vegetation Dryness Index (TVDI). This index is developed based on an empirical analysis of the relationship between land surface temperature (LST) and normalized difference vegetation index (NDVI) data. The comparison between the TVDI results and the daily precipitation data collected from meteorological stations throughout the study area indicated that there were close relationships between the two datasets. The TDVI results (values range from 0 to 1) were converted to the same unit with the AMSR-E soil moisture data (i.e., g cm-3) by linear regression analysis between these two datasets. The results achieved by this analysis were soil moisture maps that had a better spatial resolution (1 km × 1 km) than the AMSE-E soil moisture data (25 km × 25 km). The soil moisture achieved by TVDI – AMSR-E regression analysis showed the comparable spatial patterns with those from the AMSR-E soil moisture data. A quantitative analysis between the soil moisture (deduced from TVDI-AMSR-E analysis) and the AMSR-E soil moisture data also reaffirmed significant correlations between the two datasets. This study has demonstrated a method of surface soil moisture retrieval from MODIS and AMSR-E data.

Estimates of observation error based on shortwave infrared perpendicular water stress index for regional assimilation

New method for estimating the observation error in the shortwave infrared perpendicular water stress index was presented.The new method was implemented in the two stage data assimilation scheme.From the data assimilation experiment,it was demonstrated that the improved data assimilation scheme can fairly reveal spatial variations of surface soil moisture resulted from the spatial and the quantitative heterogeneous of vegetation.Thus the accuracy of the assimilation was further improved.

Reconstruction and analysis of temporal and spatial variations in surface soil moisture in China using remote sensing

An ensemble method was used to combine three surface soil moisture products, retrieved from passive microwave remote sensing data, to reconstruct a monthly soil moisture data set for China between 2003 and 2010. Using the ensemble data set, the temporal and spatial variations of surface soil moisture were analyzed. The major findings were: (1) The ensemble data set was able to provide more realistic soil moisture information than individual remote sensing products; (2) during the study period, the soil moisture increased in semiarid regions and decreased in arid regions with anoverall drying trend for the whole country; (3) the soil moisture variation trends derived from the three retrieval products and the ensemble data differ from each other but all data sets show the dominant drying trend for the summer, and that most of the drying regions were in major agricultural areas; (4) compared with the precipitation trends derived from Global Precipitation Climatology Project data, it is speculated that climate change is a possible cause for the drying trend in semiarid regions and the wetting trend in arid regions; and (5) combining soil moisture trends with land surface temperature trends derived from Moderate Resolution Imaging Spectroradiomete, the study domain was divided into four categories. Regions with drying and warming trends cover 33.2%, the regions with drying and cooling trends cover 27.4%, the regions with wetting and warming trends cover 21.1% and the regions with wetting and cooling trends cover 18.1%. The first two categories primarily cover the major grain producing areas, while the third category primarily covers nonarable areas such as Northwest China and Tibet. This implies that the moisture and heat variation trends in China are unfavorable to sustainable development and ecology conservation.

Soil moisture variability along transects over a well-developed gully in the Loess Plateau, China

Abstract Knowledge of soil moisture distributions in gullies, which are highly variable spatially and temporally, is important for both restoring vegetation and controlling erosion in them, but little attention has been paid to this spatio-temporal variability to date. Therefore, we examined soil moisture profiles and their variability along three transects traversing sidewalls of a well-developed gully with steep slopes in a hilly area of the Chinese Loess Plateau. We took intensive measurements at 20-cm intervals from 0 to 160 cm depth, using a portable time domain reflectometer, from September 3 to October 20 2009 and from April 5 to July 20 2010. The results indicate that the mean, standard deviation and coefficient of variation of moisture content vary with time, their responses to precipitation vary at different depths, and moisture content is most variable when mean values are moderate (15–20%). Revised fitting functions developed and introduced by Famiglietti et al. (2008) captured with confidence the relationship between spatial variability (SD and CV) and spatial mean of moisture content (RMSE ranging from 0.0015 to 0.0293). Soil moisture clearly varied along the transects, the vertical distribution of soil moisture differed in different seasons, and correlation analysis showed that soil texture influenced the variability of surface soil moisture more strongly than terrain attributes (except during distinct rainfall events, when this pattern reversed). The results presented here should improve understanding of spatio-temporal variations in soil moisture profiles in well-developed gullies in the Loess Plateau, and potentially elsewhere.

Spatial Variability of Surface Soil Moisture in a Depression Area of Karst Region

AbstractAn understanding of soil moisture variability is necessary to characterize the linkages between a region's hydrology, ecology, and physiography. In subtropical karst region, the spatial variability of surface soil moisture is still unclear for the rocky ecological environment and intensive land uses. The purpose of this study was to characterize the variation and patterns of soil moisture content at depth of 0–16 cm and to investigate their influencing factors in a karst depression area of southwest China. Soil moisture content was measured at 20 m intervals by intensive sampling on March 11 (dry season) and August 30 (rainy season) in 2005, respectively. Surface soil moisture presented a moderate variability in the depression area at the sampling times. The variability was relatively higher in dry season with lower mean soil moisture, but lower in rainy season with higher mean soil moisture after heavy rain event. Similar results were also obtained from the mosaic patterns of soil moisture generated by ordinary Kriging interpolation with low standard deviations. This suggested that more soil samples might be required and the sampling interval should be shortened in dry season compared with rainy season. The dominant influencing factors on the variability of surface soil moisture were rainfall and land use types. However, altitude, bare‐rock ratio, and soil organic carbon were also important factors, and exerted jointly to control and redistribute the surface soil moisture either in dry or rainy season in the depression area. Such information provided some insights for the study on eco‐hydrological processes of vegetation restoration in the karst degraded ecosystem of southwest China.

Variability of Surface Soil Moisture Observed from Multitemporal C‐Band Synthetic Aperture Radar and Field Data

The study aimed to analyze the spatial variability of surface soil moisture at different spatial scales based on field measurements and remote sensing estimates. Multitemporal Envisat satellite Advanced Synthetic Aperture Radar (ASAR) data were used to derive the surface soil moisture utilizing an empirical C‐band retrieval algorithm. Eight wide‐swath (WS) images with a spatial resolution of 150 m acquired between February and October 2008 were used to determine the surface soil moisture contents. The accuracy of the surface soil moisture retrievals was evaluated by comparison with in situ measurements. This comparison yielded a root mean square error of 5% (v/v). Based on our in situ measurements as well as remote sensing results, the relationship of the coefficient of variation of the spatial soil moisture patterns and the mean soil moisture was analyzed at different spatial scales ranging from the catchment scale to the field scale. Our results show that the coefficient of variation decreases at all scales with increasing soil moisture. The gain of this relationship decreases with scale, however, indicating that at a given soil moisture state, the spatial variation at the large scale of whole catchments is larger than at the field scale. Knowledge of the spatial variability of the surface soil moisture is important to better understand energy exchange processes and water fluxes at the land surface as well as their scaling properties.

Factors controlling the spatial variability of surface soil moisture within revegetated‐stabilized desert ecosystems of the Tengger Desert, Northern China

AbstractThe spatial structure of surface soil moisture was investigated at a grid scale with 10 × 10 m intervals on a plot of 4500 m2 in a re‐vegetated desert area in Shapotou, the Tengger Desert. The site topography varies from dune crest to dune hollow, and again to dune crest. Volumetric soil moisture contents were measured 21 times over 6 months in 2006 by using Delta‐T Theta‐Probes in the 0–6 cm surface soil layer before and after rainfall. At the same time, soil texture, relative elevation, and plant coverage were measured, to examine (i) the spatial variability of surface soil moisture; (ii) the main factors controlling the spatial variability patterns; and (iii) how the importance of these factors varies with the seasonal variations in soil moisture content. The results indicated that the normal distribution of surface soil moisture was more obvious in wet conditions than in dry conditions; the spatial variability of surface soil moisture was inherent and decreased with increased soil moisture content; and precipitation increased the spatial dependence of surface soil moisture. The relative elevation of the landscape, the shrub coverage of the community, and the soil texture were the main factors influencing surface soil moisture variability, while the effect of soil texture strengthened gradually following the heavy precipitation events. The correlation between the spatial variability of surface soil moisture and the environmental factors, such as, the dry and wet conditions, the landscape coverage and the relative elevation suggests that increasing stability of the soil moisture resulted in a significant increase of soil moisture. Copyright © 2009 John Wiley & Sons, Ltd.

Comment on “Field observations of soil moisture variability across scales” by James S. Famiglietti et al.

[1] In a recent paper, Famiglietti et al. [2008] analyzed more than 36,000 ground-based soil moisture measurements to characterize soil moisture variability across spatial scales ranging from 2.5 m to 50 km. They concluded that the relationship between soil moisture standard deviation versus mean moisture content, sq (hqi), has a convex upward behavior with maximum values occurring at mean moisture contents of 0.17 cm cm 3 and 0.19 cm cm 3 for the 800-m and 50-km scale, respectively. On the basis of these data, they derived empirical relationships between the coefficient of variation and the mean soil moisture content in order to estimate the uncertainty in field observations of mean moisture content. The authors are to be commended for providing this valuable database to the scientific community. We agree with the authors that such data are important in improving our understanding about the importance of subgrid moisture variability in the parameterization and simulation of land surface processes. However, the authors limited themselves to an empirical description of the observed data by fitting exponential relationships to the mean moisture content versus coefficient of variation (CV) data. We feel that this is a missed opportunity and would like to argue that an interpretation based on established theories and concepts in soil hydrology and upscaling theories could provide alternative methods and new insights for interpreting such data sets. Specifically, it can be shown from soil physical concepts that for a homogeneous soil, the shape of the moisture retention curve can largely explain observed variations in surface soil moisture, at any specific observation scale. For heterogeneous soils, stochastic upscaling theories may be used to relate sq (hqi) to spatial variability in soil hydraulic properties. These theories can be used to predict sq (hqi) and to examine the sensitivity of this function with respect to soil hydraulic properties.

Use of GPS receivers as a soil moisture network for water cycle studies

Measurements of soil moisture, both its global distribution and temporal variations, are required to study the water and carbon cycles. A global network of in situ soil moisture stations is needed to supplement datasets from satellite sensors. We demonstrate that signals routinely recorded by Global Positioning System (GPS) receivers for precise positioning applications can also be related to surface soil moisture variations. Over a three month interval, GPS‐derived estimates from a 300 m2 area closely match soil moisture fluctuations in the top 5 cm of soil measured with conventional sensors, including the rate and amount of drying following six precipitation events. Thousands of GPS receivers that exist worldwide could be used to estimate soil moisture in near real‐time, with L‐band signals that complement future satellite missions.

Field observations of soil moisture variability across scales

In this study, over 36,000 ground‐based soil moisture measurements collected during the SGP97, SGP99, SMEX02, and SMEX03 field campaigns were analyzed to characterize the behavior of soil moisture variability across scales. The field campaigns were conducted in Oklahoma and Iowa in the central USA. The Oklahoma study region is sub‐humid with moderately rolling topography, while the Iowa study region is humid with low‐relief topography. The relationship of soil moisture standard deviation, skewness and the coefficient of variation versus mean moisture content was explored at six distinct extent scales, ranging from 2.5 m to 50 km. Results showed that variability generally increases with extent scale. The standard deviation increased from 0.036 cm3/cm3 at the 2.5‐m scale to 0.071 cm3/cm3 at the 50‐km scale. The log standard deviation of soil moisture increased linearly with the log extent scale, from 16 m to 1.6 km, indicative of fractal scaling. The soil moisture standard deviation versus mean moisture content exhibited a convex upward relationship at the 800‐m and 50‐km scales, with maximum values at mean moisture contents of roughly 0.17 cm3/cm3 and 0.19 cm3/cm3, respectively. An empirical model derived from the observed behavior of soil moisture variability was used to estimate uncertainty in the mean moisture content for a fixed number of samples at the 800‐m and 50‐km scales, as well as the number of ground‐truth samples needed to achieve 0.05 cm3/cm3 and 0.03 cm3/cm3 accuracies. The empirical relationships can also be used to parameterize surface soil moisture variations in land surface and hydrological models across a range of scales. To our knowledge, this is the first study to document the behavior of soil moisture variability over this range of extent scales using ground‐based measurements. Our results will contribute not only to efficient and reliable satellite validation, but also to better utilization of remotely sensed soil moisture products for enhanced modeling and prediction.

Dry‐end surface soil moisture variability during NAFE'06

Characterization of the space‐time variability of soil moisture is important for land surface and climate studies. Here we develop an analytical model to investigate how, at the dry‐end of the soil moisture range, the main characteristics of the soil moisture field (spatial mean and variability, steady state distribution) depend on the intermittent character of low intensity rain storms. Our model is in good agreement with data from the recent National Airborne Field Experiment (NAFE'06) held in the semiarid Australian Murrumbidgee catchment. We find a positive linear relationship between mean soil moisture and its associated variability, and a strong dependency of the temporal soil moisture distribution to the amount and structure of precipitation.

Soil moisture mapping based on ASAR/ENVISAT radar data over a Sahelian region

The analysis of feedbacks between continental surfaces and the atmosphere is one of the key factors to understanding African Monsoon dynamics. For this reason, the monitoring of surface parameters, in particular soil moisture, is very important. Satellite remote sensing appears to be the most suitable means of obtaining data relevant to such parameters. The present paper presents a methodology applied to the mapping and monitoring of surface soil moisture over the Kori Dantiandou region in Niger, using data provided by the ASAR/ENVISAT radar instrument. The study is based on 15 sets of ASAR/ENVISAT C‐band radar data, acquired during the 2004 and 2005 rainy seasons. Simultaneously with radar acquisitions, ground soil moisture measurements were carried out in a large number of test fields. Soil moisture was estimated only for fields with bare soil or low‐density vegetation, using low‐incidence‐angle radar data (IS1 configuration). A mask was developed, using SPOT/HRV data and DTM, for use over areas characterized by high‐density vegetation cover, pools, and areas with high slopes. Soil moisture estimations are based on horizontal‐ and vertical‐polarization radar data. In order to double the temporal frequency of soil moisture estimations, IS2 data were used with IS1 data, with all data normalized to a single incidence angle. A high correlation is observed between in situ measurements and processed radar data. An empirical inversion technique is proposed, to estimate surface soil moisture from dual‐polarization data with a spatial resolution of approximately 1 km. Surface soil moisture maps are presented for all the studied sites, at various dates in 2004 and 2005. Of particular interest, these maps reveal convective precipitation scales associated with strong spatial variations in surface soil moisture.

Characteristics and Controlling Factors of Regional-scale Variability in Surface Soil Moisture within Semi-arid Grassland in Mongolia

In this study, we monitored the surface soil moisture at 15 locations within an area of 100 km × 100 km in the Mongolian steppe region. A principal component analysis was used to determine the characteristics and dominant patterns of spatiotemporal variations in the measured surface soil moisture. The results reveal that the predominant pattern of temporal variation is related to large-scale rainfall and that this was spatially uniform over the entire study area. In terms of both temporal and spatial patterns, the spatial heterogeneity of rainfall activity was a minor factor, accounting for less than 33% of the total observed variance. The most dominant pattern in the spatial distribution of surface soil moisture showed a correlation with the spatial distribution of the mean or minimum water content, and the minimum water content was largely equivalent to the wilting point, which is a soil-hydraulic index. Thus, we conclude that the most dominant pattern of surface soil moisture distribution over the study region is regulated by soil-hydraulic properties. Rainfall events acted to disturb or reset this pattern, but the pattern was re-established during drying of the soil.

Scaled spatial variability of soil moisture fields

This study identifies soil moisture spatial variability patterns using measurements across different extents (i.e., field, watershed, and basin) and depths (i.e., from surface to root zone profile) from 18 different soil moisture field experiments. The spatial variability patterns are well represented by negative exponential functions between the mean and the coefficient of variation of soil moisture. Principal component analysis demonstrates that rainfall and topography explain surface soil moisture variability changes as soils dry, while soil parameters control the maximum relative variability. Soil moisture's relative variability typically decreases as sampling extent increases, supporting the power law decay function proposed by Rodriquez‐Iturbe et al. (1995). The finding that soil moisture relative variability increases as soil depth increases is consistent with an earlier study (Choi and Jacobs, 2006). These common soil moisture variability patterns can provide a feasible methodology to validate land surface models and to estimate variability across extents from mean soil moisture values.

Assessing spatial and temporal variations in surface soil moisture in fire-disturbed black spruce forests in Interior Alaska using spaceborne synthetic aperture radar imagery — Implications for post-fire tree recruitment

Recent studies [Bourgeau-Chavez, L.L., Kasischke, E.S., Riordan, K., Brunzell, S.M., Nolan, M., Hyer, E.J., Slawski, J.J., Medvecz, M., Walters, T., and Ames, S. (in press). Remote monitoring of spatial and temporal surface soil moisture in fire disturbed boreal forest ecosystems with ERS SAR imagery. Int. J. Rem. Sens.] demonstrated that ERS SAR imagery can be used to estimate surface soil moisture in recently burned black spruce forests in interior Alaska. We used this relationship to analyze the intra- and inter-annual variations surface soil moisture in two burned black spruce forests in Alaska. The results of this study showed distinct seasonal and longer-term trends in soil moisture in the two sites, with the site that burned in 1994 having higher soil moisture than the site that burned in 1999. The differences in soil moisture between the sites were related to landscape-scale variations in soil drainage and seasonal permafrost thawing. Finally, we found that the 1999 site had dramatically lower levels of tree recruitment (both aspen and black spruce) than the 1994 site as a result of the lower soil moisture levels. These results show that the ERS SAR and similar systems can be used to monitor a site characteristic that is important to understanding changes in the ecosystem community structure that result from variations in climate and the fire regime in the boreal region.

Large area soil moisture estimation and mapping using space-borne multi-frequency passive microwave data

The paper reports the estimation of surface soil moisture (SM) using surface wetness Index (SWI) retrieved from multi-frequency passive microwave radiometer. A change detection algorithm was followed which transforms SWI variations in to SM variations using per pixel soil property of field capacity and air-dry status. Estimated soil moisture was compared with the point measurements made at the Monmouth and De Kalb sites of Illinois (USA) for the validation. Sensitivity of the SWI to the variations of rainfall at various vegetation fractions is analyzed. RMS error of volumetric soil moisture is found to be in the range of 6.35 to 8.85 %. The method works well up to the vegetation fraction of 40 %. Applications of the technique are demonstrated by the spatio-temporal analysis of estimated soil moisture maps for India. Characteristic increase in soil moisture was observed with the progress of monsoon from 25 to 32 week in northern India and 46 to 52 week in the costal parts of Tamil Nadu in south.

Characterization of footprint‐scale surface soil moisture variability using Gaussian and beta distribution functions during the Southern Great Plains 1997 (SGP97) hydrology experiment

The behavior of satellite footprint‐scale surface soil moisture probability density functions (PDF) was analyzed using 50‐km‐scale samples taken from soil moisture images collected during the Southern Great Plains 1997 (SGP97) hydrology experiment. Under the observed wetness conditions, soil moisture variability generally peaked in the midrange of mean soil moisture content and decreased toward the wet and dry ends, while in the midrange it was more widely distributed. High variability in the midrange is attributed to the multimodality of soil moisture PDFs, which apparently results from fractional precipitation within the footprint‐scale fields. Single Gaussian, single beta, and mixtures of two Gaussian distributions were utilized to fit observed footprint‐scale soil moisture distributions. As a single‐component density, the Gaussian PDF was shown to be a good choice, compared to the beta distribution, for representing spatial variability, particularly under wet conditions. The performance of the Gaussian PDF was greatly improved by using a mixture of two Gaussian distributions. Implications of this study for the validating spaceborne remotely sensed soil moisture estimates and for parameterization of subgrid‐scale surface soil moisture content in land surface models are discussed.

A field‐scale analysis of the dependence of wind erosion threshold velocity on air humidity

The wind‐forced detachment and movement of soil particles, is known for having important regional and global implications on climate change and biogeochemical cycles. The wind erosion process occurs when wind speed exceeds a certain threshold value, which depends on a number of factors, including surface soil moisture. In arid regions changes in atmospheric humidity contribute to most of the variability in surface soil moisture, which, in turn, has a significant influence on wind erosion threshold. This paper investigates the dependence of wind erosion threshold on air humidity in two arid regions in North America, the Mojave Desert (CA) and Canyonlands (UT). Field data are used to test existing models relating the threshold velocity for wind erosion to atmospheric conditions. The threshold velocity is found to be significantly dependent on the atmospheric relative humidity (RH) and to exhibit a well‐defined maximum for RH ≈ 35%. This result validates at the field scale recent theories and laboratory‐scale experimental results.

Variability of surface soil moisture at the watershed scale

Data from the Washita '92 experiment illustrate the spatial and temporal variability of surface soil moisture. Statistical moments revealed that there is no significant skewness or kurtosis in the distribution. Over the drying period the variance decreased with time; however, the coefficient of variation did not significantly change, indicating that scaled variability remains roughly constant. Regression analysis showed that soil texture, vegetation density, and time since precipitation accounted for approximately 55% of the variability of surface soil moisture. There are also strong indications of persistence in the spatial structure representing perhaps topography and solar exposure, which was maintained and explained 31% of the total variability. The methodology proposed herein should be generally applicable in the analysis of any spatially variable land surface data set as it evolves with time.