Soil Moisture Monitoring Network Research Articles

Characterizing Soil Physical Properties for Soil Moisture Monitoring with the North Carolina Environment and Climate Observing Network

Abstract Soil moisture has important implications for meteorology, climatology, hydrology, and agriculture. This has led to growing interest in development of in situ soil moisture monitoring networks. Measurement interpretation is severely limited without soil property data. In North Carolina, soil moisture has been monitored since 1999 as a routine parameter in the statewide Environment and Climate Observing Network (ECONet), but with little soils information available for ECONet sites. The objective of this paper is to provide soils data for ECONet development. The authors studied soil physical properties at 27 ECONet sites and generated a database with 13 soil physical parameters, including sand, silt, and clay contents; bulk density; total porosity; saturated hydraulic conductivity; air-dried water content; and water retention at six pressures. Soil properties were highly variable among individual ECONet sites [coefficients of variation (CVs) ranging from 12% to 80%]. This wide range of properties suggests very different behavior among sites with respect to soil moisture. A principal component analysis indicated parameter groupings associated primarily with soil texture, bulk density, and air-dried water content accounted for 80% of the total variance in the dataset. These results suggested that a few specific soil properties could be measured to provide an understanding of differences in sites with respect to major soil properties. The authors also illustrate how the measured soil properties have been used to develop new soil moisture products and data screening for the North Carolina ECONet. The methods, analysis, and results presented here have applications to North Carolina and for other regions with heterogeneous soils where soil moisture monitoring is valuable.

Maqu network for validation of satellite-derived soil moisture products

Soil moisture monitoring of the Tibetan Plateau is of primary importance for understanding land–atmosphere interactions of this region and their effects on the climate of eastern and South-East Asia. Operational satellite-derived soil moisture products, such as those obtained from AMSR-E data by VUA–NASA and ASCAT data by TU-Wien, as well as that to become available in the near future (such as data from SMOS and SMAP), can provide the information required, but their accuracy for this region needs to be evaluated before further application. For this reason, a soil moisture and temperature monitoring network was set up in the water source region of the Yellow River, in the north-eastern region of the Tibetan Plateau (Maqu county). It consists of 20 stations distributed, according to a stratified sampling, over an area of approximately 40km×80km.This study describes the Maqu network and presents the first set of data measured from July 2008 to December 2009, which shows the capability of the network to monitor the spatial and temporal soil moisture variability of the area with a high degree of accuracy. Temporal stability analyses revealed that the soil moisture spatial patterns are not always stable in time. The sites that show the highest and the most variable bias with respect to the average are located in regions with extreme soil properties, covering relatively small areas.The weighted spatial average of measured soil moisture was successfully used as ground reference for the validation of the AMSR-E soil moisture products and ASCAT soil wetness index products. For the monsoon season, overall good agreement was found between in situ time series and AMSR-E products, with a linear fit between the two datasets close to the 1:1 line and a standard error of the regression lower than 0.05. The agreement between ASCAT and in situ data was affected by several large variations of the former corresponding to little changes of the latter, thus the standard error of the regression was higher than 0.07.

Downscaling SMOS-Derived Soil Moisture Using MODIS Visible/Infrared Data

A downscaling approach to improve the spatial resolution of Soil Moisture and Ocean Salinity (SMOS) soil moisture estimates with the use of higher resolution visible/infrared (VIS/IR) satellite data is presented. The algorithm is based on the so-called “universal triangle” concept that relates VIS/IR parameters, such as the Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (Ts), to the soil moisture status. It combines the accuracy of SMOS observations with the high spatial resolution of VIS/IR satellite data into accurate soil moisture estimates at high spatial resolution. In preparation for the SMOS launch, the algorithm was tested using observations of the UPC Airborne RadIomEter at L-band (ARIEL) over the Soil Moisture Measurement Network of the University of Salamanca (REMEDHUS) in Zamora (Spain), and LANDSAT imagery. Results showed fairly good agreement with ground-based soil moisture measurements and illustrated the strength of the link between VIS/IR satellite data and soil moisture status. Following the SMOS launch, a downscaling strategy for the estimation of soil moisture at high resolution from SMOS using MODIS VIS/IR data has been developed. The method has been applied to some of the first SMOS images acquired during the commissioning phase and is validated against in situ soil moisture data from the OZnet soil moisture monitoring network, in South-Eastern Australia. Results show that the soil moisture variability is effectively captured at 10 and 1 km spatial scales without a significant degradation of the root mean square error.

Soil moisture variability and its influence on convective precipitation over complex terrain

AbstractSoil moisture monitoring data from the Convective and Orographically‐induced Precipitation Study (COPS) 2007 are combined with operational and high‐resolution simulations with the weather forecast system GME/COSMO‐DE to analyse the discrepancies between observed and modelled soil moisture fields and their potential impacts on convective precipitation forecasts. We use data from the newly installed soil moisture monitoring network comprising 47 stations with soil moisture sensors installed at three different depths within the COPS area in southwest Germany. The obtained soil moisture fields are compared to their related representation within the global model GME interpolated with high‐resolution soil and surface data to a 2.8 km resolution yielding soil moisture values on the same grid as the high‐resolution model COSMO‐DE. Systematic differences between modelled and measured soil moisture values are found and used to determine the potential impact of large soil moisture biases for the modelling of convective processes. This is achieved by conducting sensitivity tests using COSMO‐DE, in which the initialisation fields of soil moisture are varied. Results show a general mean bias towards too dry soil conditions, both in GME during the whole COPS period, as well as in the high‐resolution modelling of specific COPS IOPs with COSMO‐DE. The influence of this bias on simulated precipitation is significant and non‐trivial and depends on the specifics of the analysed case study. The results presented in this study demonstrate that soil moisture has a considerable impact on convection‐related parameters over complex and heterogeneous terrain, but that no simple relationship regarding the sign of the soil moisture–precipitation feedback could be identified due to a complex interplay between various factors that favour or inhibit convection initiation. Copyright © 2011 Royal Meteorological Society

Spatio-temporal soil moisture variability in Southwest Germany observed with a new monitoring network within the COPS domain

Within the ‘Convective and Orographically-induced Precipitation Study’ (COPS) 2007 in Southwest Germany and Northeast France a soil moisture monitoring network was installed. The aim of the network is to identify the interaction between the temporal and spatial variability of the soil moisture field and its influence on the energy balance and the moisture availability in the planetary boundary layer. The network is comprised of a large number of newly developed low-cost soil moisture sensors based on the frequency-domain reflectometry method (FDR). In total 47 soil moisture stations within the COPS domain were each equipped with two to four sensors simultaneously measuring vertical profiles of soil moisture and soil temperature down to 50 cm depth. This contribution describes the soil moisture network, its installation procedure and the calibration of the sensor output signal. Furthermore we discuss the soil texture distribution within the study area and present first analyses of the spatio-temporal soil moisture variability during a 13 month period from June 2007 till June 2008 based on regional differences and site specific properties (altitude and soil texture). Results show that the altitude plays a key role for the overall soil moisture pattern relative to the area mean due to the direct linkage to precipitation patterns. Soil texture controls the vertical soil moisture gradient relative to the near surface soil moisture, as their properties control water storage and drainage characteristics. Both factors significantly influence regional soil moisture patterns in Southwest Germany. Zusammenfassung –

Evaluation of soil moisture derived from passive microwave remote sensing over agricultural sites in Canada using ground-based soil moisture monitoring networks

Passive microwave soil moisture datasets can be used as an input to provide an integrated assessment of climate variability as it relates to agricultural production. The objective of this research was to examine three passive microwave derived soil moisture datasets over multiple growing seasons in contrasting Canadian agricultural environments. Absolute and relative soil moisture was evaluated from two globally available datasets from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) sensor using different retrieval algorithms, as well as relative soil wetness at a weekly scale from the Special Sensor Microwave/Imager (SSM/I) sensor. At a daily scale, the Land Parameter Retrieval Model (LPRM) provides a better estimate of surface soil moisture conditions than the National Snow and Ice Data Center (NSIDC) dataset, with root mean squared errors ranging from 5 to 10% for LPRM and 12 to 18% for NSIDC soil moisture when a temporal smoothing is applied to the dataset. Both datasets provided better estimates of soil moisture over the temperate site near Elora, Ontario than the prairie site near Davidson, Saskatchewan. The LPRM dataset tends to overestimate soil moisture conditions at both sites, where the NSIDC dataset tends to underestimate absolute soil moisture. These differences in retrieval methods were independent of radiometric frequency used. At weekly scales, the LPRM dataset provides a better relative estimate of wetness conditions when compared to the NSIDC and the Basist Wetness Index (BWI) from SSM/I data, but the SSM/I dataset did provide a reasonably good relative indicator of moisture conditions. The high variability in accuracy of soil moisture estimation related to retrieval algorithms indicates that consistency is needed in these datasets if they are to be integrated in long term studies for yield estimation or data assimilation.

Illinois Soil Moisture under Sod Experiment

Abstract Long-term data collection of volumetric soil moisture under sod has been conducted in Illinois for more than 25 years. Numerous applied and modeling studies have been undertaken with these data, often relating results to regional conditions under a variety of surface covers. However, the actual level of representation of these data to nearby areas with different surface covers is unknown. In 2006/07, the Soil Moisture under Sod Experiment was conducted at Bondville, Illinois, to increase understanding of soil moisture variability across a very small area of seemingly uniform surface and near-surface conditions. Ten locations were chosen at random within a 5.9-ha sodded field for twice weekly neutron probe soil moisture observations over a period of more than 13 months. Measurements were taken at the surface and at 20-cm intervals down to 2 m, precisely matching the historic Illinois depth observations. A detailed surface terrain analysis was conducted to consider effects on soil moisture attributable to surface slope or ponding potential at each monitoring location across the very low relief surface. The near-surface water table level at the field location was monitored. At the end of observations, soil property heterogeneity (i.e., soil porosity, bulk density, and soil color) was determined by digging trenches and extracting soil cores immediately adjacent to each monitoring site at all observation levels within the predominantly loess soil. Results indicate a strong temporal consistency in intrasite trends of volumetric soil moisture at all depths throughout the experiment. However, intersite spatial variability increased with depth, indicated by an average standard deviation of all temporal observations of 2.26% in the top 30 cm of soil and 5.19% in the 170–200-cm layer. Differences between the average field soil moisture at all primary randomly selected sites and the historic Bondville site were 2.39% and 6.51%, respectively. In addition, an apparent strong relationship was observed between soil moisture in deeper layers and surface terrain slope, and to a lesser extent with soil porosity and bulk density. The question of representativeness of soil moisture under sod to adjacent surface covers was not answered with this work, but the large differences measured across this seemingly uniform field suggest that proper use of the historic Illinois dataset by future research related to adjacent areas may need greater attention. Most of Illinois is under an agricultural cover, not sod. Adequate data monitoring of surface terrain slope, soil profiles, and water table climatology under various major surface covers within a region may be necessary prior to the installation of new soil moisture monitoring networks and before useful assumptions concerning spatial representation can be made that attribute individual soil moisture datasets to adjacent areas. These results highlight the importance of a strict globally unified protocol for soil moisture network design and data collection in support of quality in situ global soil moisture assessment, a primary goal of the International Soil Moisture Working Group of the Global Energy and Water Cycle Experiment.

Spatial‐temporal variability of soil moisture and its estimation across scales

The soil moisture is a quantity of paramount importance in the study of hydrologic phenomena and soil‐atmosphere interaction. Because of its high spatial and temporal variability, the soil moisture monitoring scheme was investigated here both for soil moisture retrieval by remote sensing and in view of the use of soil moisture data in rainfall‐runoff modeling. To this end, by using a portable Time Domain Reflectometer, a sequence of 35 measurement days were carried out within a single year in seven fields located inside the Vallaccia catchment, central Italy, with area of 60 km2. Every sampling day, soil moisture measurements were collected at each field over a regular grid with an extension of 2000 m2. The optimization of the monitoring scheme, with the aim of an accurate mean soil moisture estimation at the field and catchment scale, was addressed by the statistical and the temporal stability. At the field scale, the number of required samples (NRS) to estimate the field‐mean soil moisture within an accuracy of 2%, necessary for the validation of remotely sensed soil moisture, ranged between 4 and 15 for almost dry conditions (the worst case); at the catchment scale, this number increased to nearly 40 and it refers to almost wet conditions. On the other hand, to estimate the mean soil moisture temporal pattern, useful for rainfall‐runoff modeling, the NRS was found to be lower. In fact, at the catchment scale only 10 measurements collected in the most “representative” field, previously determined through the temporal stability analysis, can reproduce the catchment‐mean soil moisture with a determination coefficient, R2, higher than 0.96 and a root‐mean‐square error, RMSE, equal to 2.38%. For the “nonrepresentative” fields the accuracy in terms of RMSE decreased, but similar R2 coefficients were found. This insight can be exploited for the sampling in a generic field when it is sufficient to know an index of soil moisture temporal pattern to be incorporated in conceptual rainfall‐runoff models. The obtained results can address the soil moisture monitoring network design from which a reliable soil moisture temporal pattern at the catchment scale can be derived.

Towards a general equation for frequency domain reflectometers

Summary It is well documented that capacitance-based soil moisture sensor measurements are particularly influenced by particle size distribution, density, salinity, and temperature of a soil, in addition to its moisture content. Moreover, the equations provided by manufacturers of soil moisture sensors are often only applicable to a limited number of soil types, thus yielding significant errors when compared with gravimetric measurements for observations in real soils. This limitation makes site-specific calibrations of such sensors necessary. Consequently, development of a general equation provides the possibility to derive the needed parameters from information such as soil type or particle size distribution. This paper describes the development of a general equation for the Campbell Scientific CS616 Water Content Reflectometers using data from sensors installed throughout the Goulburn River experimental catchment. It is subsequently tested using monitoring sites in the Murrumbidgee Soil Moisture Monitoring Network, which were not part of the original development; both monitoring networks are located in south-eastern Australia. Previously developed equations for temperature correction and soil moisture estimation using the Campbell Scientific CS615 Water Content Reflectometer are adapted to the new CS616 sensor. Moreover, relationships between readily available soil properties and the parameters of the general equations are derived. It is shown that the general equations developed here can be applied to data collected in the field using only information on the soil particle size distribution with an RMSE of around 6% m3/m3 (

Variability and uncertainty in spatial, temporal and spatiotemporal crop-yield and related data.

Application of the theories of precision agriculture to the practicalities of broad-acre farming relies on successful handling of the ramifications of uncertainty in information, i.e. information pertaining to the spatial and temporal variation of those factors which determine yield components and/or environmental losses. This paper discusses the uncertainty of yield and related variables as measured by their spatial and temporal variance. The magnitude of these two components gives a suggestion as to the appropriate scale of management. Simultaneous reporting on spatial and temporal variation is rare and the theory of these types of process is still in its infancy. Some brief theory is presented, followed by several examples from the Rothamsted classic experiments, yield-monitoring experiments in Australia, a long-term barley trial in Denmark, and a soil moisture monitoring network. It is clear that annual temporal variation is much larger than the spatial variation within single fields. This leads to the conclusion that if precision agriculture is to have a sound scientific basis and ultimately a practical outcome then the null hypothesis that still remains to be seriously researched is: 'given the large temporal variation in yields relative to the scale of a single field, then the optimal risk aversion strategy is uniform management.'