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Soil moisture will be mapped globally by the European Soil Moisture and Ocean Salinity (SMOS) mission to be launched in 2009. The expected soil moisture accuracy will be 4.0 %v/v. The core component of the SMOS soil moisture retrieval algorithm is the L-band Microwave Emission of the Biosphere (L-MEB) model which simulates the microwave emission at L-band from the soil–vegetation layer. The model parameters have been calibrated with data acquired by tower mounted radiometer studies in Europe and the United States, with a typical footprint size of approximately 10 m. In this study, aircraft L-band data acquired during the National Airborne Field Experiment (NAFE) intensive campaign held in South-eastern Australia in 2005 are used to perform the first evaluation of the L-MEB model and its proposed parameterization when applied to coarser footprints (62.5 m). The model could be evaluated across large areas including a wide range of land surface conditions, typical of the Australian environment. Soil moisture was retrieved from the aircraft brightness temperatures using L-MEB and ground measured ancillary data (soil temperature, soil texture, vegetation water content and surface roughness) and subsequently evaluated against ground measurements of soil moisture. The retrieval accuracy when using the L-MEB ‘default’ set of model parameters was found to be better than 4.0 %v/v only over grassland covered sites. Over crops the model was found to underestimate soil moisture by up to 32 %v/v. After site specific calibration of the vegetation and roughness parameters, the retrieval accuracy was found to be equal or better than 4.8 %v/v for crops and grasslands at 62.5-m resolution. It is suggested that the proposed value of roughness parameter H R for crops is too low, and that variability of H R with soil moisture must be taken into consideration to obtain accurate retrievals at these scales. The analysis presented here is a crucial... [Copyright &y& Elsevier]

Abstract: 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 1km resolution 11 times (every 1–3 days) over a 40 by 55km area in the Yanco region and 3 times over a 40 by 50km 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 1km resolution was up to 10% vol. and (iii) the validation of 1km 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 1km 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. [Copyright &y& Elsevier]

The National Airborne Field Experiment 2005 (NAFE'05) and the Campaign for validating the Operation of Soil Moisture and Ocean Salinity (CoSMOS) were undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. The objective of the joint campaign was to provide simulated Soil Moisture and Ocean Salinity (SMOS) observations using airborne L-band radiometers supported by soil moisture and other relevant ground data for the following: 1) the development of SMOS soil moisture retrieval algorithms; 2) developing approaches for downscaling the low-resolution data from SMOS; and 3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and CoSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km x 40 km coverage flights at 500-rn and 1-km resolution for the simulation of a SMOS pixel, multiresolution flights with ground resolution ranging from 1 km to 62.5 m, multiangle observations, and specific flights that targeted the vegetation dew and sun glint effect on L-band soil moisture retrieval. The L-band data were accompanied by airborne thermal infrared and optical measurements. The ground data consisted of continuous soil moisture profile measurements at 18 monitoring sites throughout the 40 km x 40 km study area and extensive spatial near-surface soil moisture measurements concurrent with airborne monitoring. Additionally, data were collected on rock coverage and temperature, surface roughness, skin and soil temperatures, dew amount, and vegetation water content and biomass. These data are available at www.nafe.unimelb. edu.au. [ABSTRACT FROM AUTHOR]

A number of automated techniques for point measurement of soil moisture content have been developed to an operational level over the past few decades. While each of those techniques have been individually evaluated by the thermogravimetric (oven drying and weighing) method, typically under laboratory conditions, there have been few studies which have made a direct comparison between the various techniques, particularly under field conditions. This paper makes an inter-comparison of the Virrib®, Campbell Scientific CS615 reflectometer, Soil Moisture Equipment Corporation TRASE® buriable- and connector-type time domain reflectometry (TDR) soil moisture sensors, and a comparison of the connector-type TDR sensor with thermogravimetric measurements for data collected during a 2-year field study. Both qualitative and quantitative comparisons between the techniques are made, and comparisons made with results from a simple water balance ‘bucket’ model and a Richards equation based model. It was found that the connector-type TDR sensors produced soil moisture measurements within the ±2.5% v/v accuracy specification of the manufacturer as compared to thermogravimetric data when using the manufacturer''s calibration relationship. However, comparisons with the water balance model showed that Virrib and buriable-type TDR sensors yielded soil moisture changes that exceeded rainfall amounts during infiltration events. It was also found that the CS615 reflectometer yielded physically impossible soil moisture measurements (greater than the soil porosity) during periods of saturation. Moreover, the buriable-type TDR measurements of soil moisture content were systematically less than the Virrib measurements by approximately 10% v/v. In addition to the good agreement with thermogravimetric measurements, the connector-type TDR soil moisture measurements yielded the best agreement with Richards equation based model predictions of soil moisture content, with Virrib sensors yielding a poor agreement in the deeper layers. This study suggests that connector-type TDR sensors give the most accurate measurements of soil moisture content out of the sensor types tested. [Copyright &y& Elsevier]

Source control measures include rainwater tanks, infiltration trenches, grassed swales, detention basins and constructed wetlands that can be used in housing allotments and subdivisions. A methodology for evaluating the regional economic benefits due to implementation of source control measures is presented and illustrated for two case studies in the Lower Hunter and Central Coast regions of New South Wales, Australia. It is demonstrated that use of rainwater tanks to supplement mains water supply for toilet, hot water and outdoor uses can very significantly reduce demand on mains water supply. Reductions in regional water demand will enable deferment of water supply headworks augmentation, while reductions in peak mains water demand will extend the life of water supply distribution infrastructure. In addition, substantial reduction of stormwater discharge from allotments can be realised. For the Lower Hunter region with an urban population of about 450,000 it is shown that construction of new water supply headworks infrastructure can be delayed by up to 34 years. Compared with the traditional provision of mains water and stormwater disposal, the use of rainwater tanks along with other source control measures can produce present worth savings to the Lower Hunter region conservatively estimated to be up to $67 million. Similar results were found for the Central Coast region. [Copyright &y& Elsevier]

The Kalman filter assimilation technique is applied to a simplified soil moisture model for retrieval of the soil moisture profile from near-surface soil moisture measurements. First, the simplified soil moisture model is developed, based on an approximation to the Buckingham–Darcy equation. This model is then used in a 12-month one-dimensional field application, with updating at 1-, 5-, 10-, and 20-day intervals. The data used are for the Nerrigundah field site, New South Wales, Australia. This study has identified (i) the importance of knowing the depth over which the near-surface soil moisture measurements are representative (i.e., observation depth), (ii) soil porosity and residual soil moisture content as the most important soil parameters for correct retrieval of the soil moisture profile, (iii) the importance of a soil moisture model that represents the dominant soil physical processes correctly, and (iv) an appropriate forecasting model as far more important than the temporal resolution of near-surface soil moisture measurements. Although the soil moisture model developed here is a good approximation to the Richards equation, it requires a root water uptake term or calibration to an extreme drying event to model extremely dry periods at the field site correctly. [ABSTRACT FROM AUTHOR]

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 (<1%m3/m3 under laboratory conditions; which is a significant improvement in comparison to 14%m3/m3 when using the manufacturer’s equations). [Copyright &y& Elsevier]