International Journal of Remote Sensing Vol. 32, No. 19, 10 October 2011, 5443–5460
Statistical properties of soil moisture images derived from Radarsat-1 SAR data A. MERZOUKI*†, A. BANNARI‡, P. M. TEILLET§ and D. J. KING¶ †Research Branch, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada ‡Geography Department, PO Box 450, University of Ottawa, Ottawa, ON, K1N 6N5, Canada §Department of Physics, University of Lethbridge, 4401 University Drive West, Lethbridge, AB, T1K 3M4, Canada ¶Department of Geography and Environmental Studies, Carleton University, 1112 Colonel By Drive, Ottawa, ON, K1S 5B6, Canada (Received 28 January 2008; in final form 30 August 2009) In this article, we report on the assessment of the spatial variability of soil moisture using synthetic aperture radar (SAR) data. The imagery was acquired during five different periods over the Roseau River watershed in southern Manitoba, Canada. For validation purposes, ground measurements were carried out at 62 locations simultaneous with the satellite data acquisitions. The first step in this analysis was to assess the performance of the Integral Equation Model (IEM) in simulating backscatter coefficients for selected bare soils. In order to reduce the surface roughness effect on radar backscatter, a semi-empirical calibration technique was implemented. This calibrated model was then implemented in a simplex inversion routine in order to estimate and map soil moisture. Derived spatial patterns of near-surface moisture content were then examined using scale analyses. It has been confirmed that the variance of radar-based soil moisture images follows power law decay versus the observation scale. Also, more explicit analysis of the same soil moisture maps shows a ln–ln linear spatial scale with statistical moments. Concave shape dependency of the corresponding slopes with the moment order was observed during all radar acquisition periods. The latter indicates the presence of multifractal effects.
1. Introduction Soil moisture is a key variable in many environmental sciences, including hydrology, climatology and agriculture. Even though it represents a small proportion of the liquid freshwater on Earth (0.15%), it plays an important role in controlling the partitioning of precipitation into ground water storage and runoff (Jackson et al. 1996). In addition, it modulates interactions between the land surface and the atmosphere, thereby influencing climate and weather (Entekhabi 1995). Accurate modelling of the above processes depends on the ability to provide proper spatial characterization of soil
*Corresponding author. Email:
[email protected] International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online © 2011 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/01431161.2010.502154
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moisture. Quantifying the spatial variability of soil moisture is necessary in interpreting land–atmosphere surface interaction and related hydrologic phenomena (Cosh and Brutsaert 1999). Examples are present showing its importance including upscaling soil moisture to the resolution level of general circulation models (Hu et al. 1997, Oldak et al. 2002) and downscaling the same parameter from the coarse remote sensor resolution (Crow and Wood 1999). Some studies have attempted to describe the spatial heterogeneity of soil moisture content by using different probability density functions such as: gamma (Entekhabi and Eagleson 1989), normal (Wood and Lakshmi 1993), and uniform distributions (Bonan et al. 1993). These studies failed to capture the spatial characteristics inherent to soil moisture variability because in such descriptions soil moisture is assumed to have spatially independent identical distribution. Previous scaling studies were conducted to characterize the spatial structure of soil moisture using an L-band passive microwave mapping instrument – the Electronically Scanned Thinned Array Radiometer (ESTAR) (Rodríguez-Iturbe et al. 1995, Hu et al. 1997, Oldak et al. 2002). Using data from the Washita’92 experiment, which took place in the Little Washita River watershed in Oklahoma (USA), as described in Jackson et al. (1995), RodríguezIturbe et al. (1995) found evidence of clear spatial structure in soil moisture images and provided valuable indices to develop preliminary insight about the characterization of the associated spatial structure using the theory of statistical self-similarity. They concluded that the variance of the soil moisture fields exhibited a power law decay as a function of the area where the data were collected, therefore reaching a range of 1 km2 in terms of resolution. Using the same soil moisture data, Hu et al. (1997) extended the validity range of this power law dependence over the range of resolutions up to 32 km2 . They also showed that the soil moisture does not follow simple scaling for moments from the second to the sixth. Oldak et al. (2002) arrived at similar conclusions while investigating the scaling properties of remotely sensed soil moisture data over the same area using data collected during the Southern Great Plains (SGP97) hydrological experiment (Jackson et al. 1999). Experiments undertaken in the 1970s demonstrated the sensitivity of radar backscatter to soil moisture conditions (Ulaby et al. 1974). Recent advances in active microwave remote sensing have confirmed the potential for using radar data for the generation of soil moisture maps at different scales (Dubois et al. 1995, Baghdadi et al. 2002, Le Hégarat-Mascle et al. 2002, Zribi and Dechambre 2002). A large number of studies have been undertaken in order to establish a relationship between the observed synthetic aperture radar (SAR) response and the soil parameters. Synthetic aperture radar data based on a single configuration, such as ERS-1/2 (European Remote Sensing) with VV polarization and a 23◦ incidence angle, and Radarsat-1 with HH polarization and incidence angles ranging from 20◦ to 50◦ , have been used for retrieving both soil moisture and surface roughness (Oh et al. 1992, Fung 1994, Dubois et al. 1995, Baghdadi et al. 2002, Le Hégarat-Mascle et al. 2002, Zribi and Dechambre 2002, Srivastava et al. 2003). From an application perspective, the retrieval of such parameters requires the use of theoretical, empirical or semi-empirical models. In this study, The focus was on one of the most widely used models for retrieving soil surface parameters, which is adapted to randomly rough dielectric surfaces (Fung et al. 1992): the Integral Equation Model (IEM). In a broad sense, it can be applied to simulate the backscattering behaviour for the wide range of surface roughness values usually encountered in agricultural settings.
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This article reports on an investigation of the potential of Radarsat-1 C-band SAR to provide accurate spatial characterization of soil moisture. Initially, comparison was made between IEM simulation results and SAR responses. In order to reduce the surface roughness effect on radar backscatter behaviour, the semi-empirical calibration technique proposed by Baghdadi et al. (2004) was implemented for a first dataset covering three SAR image acquisitions. A second dataset was used for validation purposes. Then, a simplex algorithm was applied to experimental data to extract soil moisture maps. The last and main objective of this article is to examine how the apparent statistical moments of soil moisture change with measurement scale. This allows quantitative assessment of the spatial clustering bias by analysing the relationship between multi-scale effects and soil moisture saturation regimes. 2. Datasets 2.1 Study area The study area is located in the Roseau River watershed, which is a part of the Red River basin (figure 1). This area covers approximately 2400 km2 in Southern Manitoba, the remainder of the basin is in Minnesota. The Roseau River follows a general north-westerly course covering about 290 km from upstream to mouth. It crosses the Canada–US boundary at about the midpoint of its course and enters the Red River about 24 km north of the border. Sprague and Pine Creeks, which are located in the eastern headwaters of the watershed, are the only major Roseau River tributaries originating in Canada. Within the Roseau River basin the only abrupt changes in relief are Beltrami Island, in Minnesota, which is a sand ridge in the southeast portion of the basin, and the Sandilands Upland in the northeastern sector, in Manitoba. Apart from a few abrupt elevation changes in the northeast sector of the Canadian portion of this watershed, the main valley is quite flat with few prominent topographic features. Thus, the drainage effect is very low and water-saturated soils are characteristic of the Roseau River basin (Dechamps 2004). A variety of soil types are present throughout the study area, including organic soils, silty loams, sandy soils and clay soils. The western portion is dominated by rich clay soils, ideal for agriculture. The soil gradually changes to silty loams with pockets of sandy soils going eastwards. The eastern portion consists of silty to clay loams. In general the climate of south-eastern Manitoba is classified as sub-humid to humid continental with resultant extreme temperature variations (Oswald et al. 1999). Annually, most of the precipitation is in summer rather than winter. Approximately 75% of the 50 cm of average annual precipitation occurs from April to September. However, there are weather conditions that periodically promote widespread flooding through the valley (Oswald et al. 1999). Flooding and poor land drainage has delayed regional development in the basin. Canadian remedial measures to protect the low, flat, flood-prone areas north of the international boundary to Gardenton, Manitoba, consist of the Gardenton Floodway and its associated dykes and control structure. Drainage improvements have been undertaken between the floodway and Dominion City for purposes of agricultural development, and for highway and secondary road construction (IRREB 1975). 2.2 Data description 2.2.1 SAR data and image pre-processing. Radarsat-1 is equipped with a SAR operating at 5.3 GHz (C-band) with horizontal–horizontal (HH) polarization. The
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Figure 1. Sampling sites and monitoring station locations in the Canadian portion of the Roseau River basin.
wide-beam mode (W1) was selected for this study because its swath covers the full Roseau River basin, with an incidence angle ranging from 20◦ to 31◦ and a nominal spatial resolution of 30 m. The ascending orbit for Radarsat-1 has an early evening local crossing time and was selected to avoid the effect of early morning dew on the backscatter process. Radar images were acquired during five field campaigns from October 2002 to June 2003 (table 1). The SAR images were calibrated to backscatter coefficients using Shepherd’s method equation (Shepherd 2000). This procedure was implemented using the SARSIGM module included in Geomatica® software (PCI Geomatics, Richmond Hill, ON, Canada), which automatically generates the calibration data. In a previous study, we demonstrated that no significant differences or improvements in IEM simulation results were obtained using different
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Table 1. Radarsat-1 synthetic aperture radar (SAR) image acquisition details for the Roseau River basin study site.
First dataset Second dataset
Acquisition date
Day of year
Acquisition time (UTC)
1 October 2002 25 October 2002 11 April 2003 5 May 2003 22 June 2003
274 298 101 125 173
0:01:50 0:01:48 0:02:44 0:02:44 0:02:45
Scene centre (latitude/longitude) 48◦ 48◦ 48◦ 48◦ 48◦
52 54 55 55 55
N / 95◦ N / 95◦ N / 95◦ N / 95◦ N / 95◦
56 56 57 57 57
W W W W W
adaptive and non-adaptive speckle filtering methods (Merzouki 2007). This might be a result of the scene characteristics, which are assumed to be very homogeneous. Thus, a simple averaging convolution window of 5 × 5 samples was applied to the SAR scenes. These images were then geometrically corrected and georeferenced using a set of 20 ground control points (GCPs) marked on a georeferenced 15 m panchromatic Landast-7 Enhanced Thematic Mapper Plus (ETM+) image. The control points were evenly distributed and the average root-mean-square error was found to be less than one pixel in both the X and Y directions for all SAR images. The image registrations were performed using a second order polynomial transformation and nearest neighbour resampling. 2.2.2 Field selection and ground data acquisition. Apart from soil moisture and surface roughness, radar backscatter from agricultural fields is also affected by vegetation cover, plant water content and crop residue (De Roo et al. 2001, Zribi et al. 2003). Hence, for this study, only bare fields were selected. The general criteria for field selection were: good distribution across the basin, three to four fields per soil type, and large homogeneous fields (200 × 200 m) with minimal crop residue. Therefore, for the selected fields, soil moisture and surface roughness effects will dominate the backscatter response. A total of eight measurement stations were installed in September 2002 and remained in their original location until June 2003 (figure 1). These stations continuously measured soil moisture, soil temperature, air temperature, wind speed, wind direction, snow depth, radiation and precipitation. A Campbell Scientific station (Campbell Scientific Ltd., Loughborough, UK) was installed in each of the three representative areas. Five ADCON meteorological stations (Adcon Telemetry GmbH, Klosterneuburg, Austria) with telemetry and storage devices plus a base station were deployed in the basin, constituting an autonomous wireless sensorweb prototype (Teillet et al. 2003) that sent data in real time to a workstation in Ottawa. Field measurement campaigns were carried out on SAR data acquisition dates. The measurements included soil moisture content and surface roughness. A total of 22 field samples were used in autumn 2002 and 14 fields in spring 2003 (figure 1). In most cases, the fields selected in September 2002 were bare until May 2003. In the spring, new fields were selected to replace those that had been seeded, had emerging crops, or were overgrown with weeds. Significantly fewer fields were available in 2003 because of crop losses due to flooding in June 2002. Surface roughness measurements were taken at 62 sites in the selected fields using the SRM-200 surface roughness meter (Kosaka Laboratory Ltd., Tokyo, Japan)
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(Johnson et al. 1993). This instrument consists of a tripod mounted with a camera, flash and projector. The tripod is draped with a shroud to create a portable darkroom. A rectangle of light (50 × 100 mm) is projected onto the ground when the flash is activated and an oblique photograph of the ground is taken. The photograph is subsequently scanned and special software used to extract surface roughness parameters using a 0.5 cm sampling interval (Johnson et al. 1993): RMS height (s) and correlation length (l ). Measurements were taken in the look direction of the radar (74◦ azimuth with respect to true north). Care was taken to select sampling locations representative of the entire field. Three sampling points were established for each site. The sampling points were a minimum of 5 m apart. Two measurements were obtained at each sampling point, thus yielding six surface roughness profiles per site. Near-surface volumetric soil moisture content was measured for the top 6 cm for all sites using a portable Time Domain Reflectometer (TDR). Five sampling points were established along a transect line at each site. The sampling points were at least 5 m apart. Readings at each point were taken in replicates of three within a 1m radius. This resulted in a total of 15 soil moisture samples per site. Meteorological constraints were taken into consideration to achieve unbiased sampling results. Radarsat-1 acquisitions and soil sampling were not undertaken during periods of intense rainfall, prolonged freezing, or in snow cover. More details on the data collection design can be found in Deschamps (2004). 3. Background to the radar backscattering model For a given radar configuration, the backscatter models simulate the backscatter coefficient of a surface from its physical properties. In this study, a model based on the theory of electromagnetic wave scattering from a rough surface under simplifying assumptions has been used: the Integral Equation Model (IEM). This model is quite adapted to randomly dielectric rough surfaces (Fung et al. 1992). It is based on analytical solutions of the integral equations for tangential surface fields and accounts for both single and multiple surface diffusion phenomena. In a broad sense, it can be applied to simulate the backscattering behaviour in a wide range of roughness values that are usually encountered for agricultural surfaces. The validity domain of the √ IEM is defined by a set of inequalities such that (Fung 1994): ks < 3, k2 ls < μ εr and [(k s cos θ )2 (0.46kl)0.5 ] × exp{−(0.92kl(1 − sin θ ))0.5 }
