Estimation of near surface soil moisture in a sloping terrain of a Himalayan watershed using ENVISAT ASAR multi‐incidence angle alternate polarisation data

Soil moisture is one of the important input variables in hydrological and water erosion models. The extraction of information on near surface soil moisture from synthetic aperture radar (SAR) is well established mostly for flat terrain and using low incidence angle single polarisation data. The ENVISAT advanced SAR (ASAR) data available in multiple incidence angles and alternate polarisation modes were investigated in this study for soil moisture estimation in sloping terrain. The test site was Sitla Rao watershed in the Lesser Himalayas of northern India. Empirical models were developed to estimate near surface soil moisture in bare agricultural fields using alternate polarisation ASAR data. Both soil moisture and surface roughness field measurements were performed during the satellite passes. Backscatter from medium incidence angle (IS‐4) and vertical‐vertical (VV) polarisation signal is correlated better with volumetric soil moisture content compared to other incidence angles. The model parameters were further improved, and soil moisture estimation was refined by combining medium incidence angle (IS4) vertical‐horizontal polarisation response as another variable along with VV polarisation response. The effect of slope on the radar backscatter was minimized by incorporating local incidence angles derived from an ASTER DEM. Copyright © 2012 John Wiley & Sons, Ltd.     

Active microwave remote sensing for soil moisture measurement: a field evaluation using ERS‐2

Active microwave remote sensing observations of backscattering, such as C‐band vertically polarized synthetic aperture radar (SAR) observations from the second European remote sensing (ERS‐2) satellite, have the potential to measure moisture content in a near‐surface layer of soil. However, SAR backscattering observations are highly dependent on topography, soil texture, surface roughness and soil moisture, meaning that soil moisture inversion from single frequency and polarization SAR observations is difficult. In this paper, the potential for measuring near‐surface soil moisture with the ERS‐2 satellite is explored by comparing model estimates of backscattering with ERS‐2 SAR observations. This comparison was made for two ERS‐2 overpasses coincident with near‐surface soil moisture measurements in a 6 ha catchment using 15‐cm time domain reflectometry probes on a 20 m grid. In addition, 1‐cm soil moisture data were obtained from a calibrated soil moisture model. Using state‐of‐the‐art theoretical, semi‐empirical and empirical backscattering models, it was found that using measured soil moisture and roughness data there were root mean square (RMS) errors from 3·5 to 8·5 dB and r² values from 0·00 to 0·25, depending on the backscattering model and degree of filtering. Using model soil moisture in place of measured soil moisture reduced RMS errors slightly (0·5 to 2 dB) but did not improve r² values. Likewise, using the first day of ERS‐2 backscattering and soil moisture data to solve for RMS surface roughness reduced RMS errors in backscattering for the second day to between 0·9 and 2·8 dB, but did not improve r² values. Moreover, RMS differences were as large as 3·7 dB and r² values as low as 0·53 between the various backscattering models, even when using the same data as input. These results suggest that more research is required to improve the agreement between backscattering models, and that ERS‐2 SAR data may be useful for estimating fields‐scale average soil moisture but not variations at the hillslope scale. Copyright © 2004 John Wiley & Sons, Ltd.     

Use of multi‐platform,multi‐temporal remote‐sensing data for calibration of a distributed hydrological model: an application in the Arno basin,Italy

Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS‐2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring–summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat‐ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd.     

TEMPORAL MONITORING OF SOIL MOISTURE USING ERS-1 SAR DATA

Multi-temporal synthetic aperture radar (SAR) imagery from the European Remote Sensing Satellite (ERS-1) was evaluated for monitoring soil moisture at the Romney Marsh test site as part of the UK SAR Calibration and Crop Backscatter Experiment. A total of 18 C-band (5.3 GHz) ERS-1 SAR images were acquired during the three day orbit and co-registered. Accurate calibration of the backscatter measurements was achieved using calibration constants derived from an analysis of corner reflector target responses. Mean backscatter measurements were recorded for each field and compared with field data on soil moisture, surface roughness and rainfall patterns. A comparison of daily and hourly rainfall and soil moisture measurements with backscatter for different cover types showed that the observed trends in backscatter are dominated by moisture effects. A high positive correlation between volumetric soil moisture in the range 10–40% was observed for bare soil fields. A much weaker positive relationship between soil moisture and backscatter was observed for grassland fields.     

The effect of roughness in simultaneously retrieval of land surface parameters

Using remotely-sensed data, various soil moisture estimation models have been developed for bare soil areas. Previous studies have shown that the brightness temperature (BT) measured by passive microwave sensors were affected by characteristics of the land surface parameters including soil moisture, vegetation cover and soil roughness. Therefore knowledge of vegetation cover and soil roughness is important for obtaining frequent and global estimations of land surface parameters especially soil moisture.In this study, a model called Simultaneous Land Parameters Retrieval Model (SLPRM) that is an iterative least-squares minimization method is proposed. The algorithm estimates surface soil moisture, land surface temperature and canopy temperature simultaneously in vegetated areas using AMSR-E (Advance Microwave Scanning Radiometer-EOS) brightness temperature data. The simultaneous estimations of the three parameters are based on a multi-parameter inversion algorithm which includes model construction, calibration and validation using observations carried out for the SMEX03 (Soil Moisture Experiment, 2003) region in the South and North of Oklahoma.Roughness parameter has also been included in the algorithm to increase the soil parameters retrieval accuracy. Unlike other methods, the SLPRM method works efficiently in all land covers types.The study focuses on soil parameters estimation by comparing three different scenarios with the inclusion of roughness data and selects the most appropriate one. The difference between the resulted accuracies of scenarios is due to the roughness calculation approach.The analysis on the retrieval model shows a meaningful and acceptable accuracy on soil moisture estimation according to the three scenarios.The SLPRM method has shown better performance when the SAR (Synthetic Aperture RADAR) data are used for roughness calculation.     

Improvement of TOPLATS‐based discharge predictions through assimilation of ERS‐based remotely sensed soil moisture values

In this paper, we investigate the possibility to improve discharge predictions from a lumped hydrological model through assimilation of remotely sensed soil moisture values. Therefore, an algorithm to estimate surface soil moisture values through active microwave remote sensing is developed, bypassing the need to collect in situ ground parameters. The algorithm to estimate soil moisture by use of radar data combines a physically based and an empirical back‐scatter model. This method estimates effective soil roughness parameters, and good estimates of surface soil moisture are provided for bare soils. These remotely sensed soil moisture values over bare soils are then assimilated into a hydrological model using the statistical correction method. The results suggest that it is possible to determine soil moisture values over bare soils from remote sensing observations without the need to collect ground truth data, and that there is potential to improve model‐based discharge predictions through assimilation of these remotely sensed soil moisture values. Copyright © 2002 John Wiley & Sons, Ltd.     

First‐year sea ice spring melt transitions in the Canadian Arctic Archipelago from time‐series synthetic aperture radar data, 1992–2002

This paper synthesizes 10‐years' worth of interannual time‐series space‐borne ERS‐1 and RADARSAT‐1 synthetic aperture radar (SAR) data collected coincident with daily measurement of snow‐covered, land‐fast first‐year sea ice (FYI) geophysical and surface radiation data collected from the Seasonal Sea Ice Monitoring and Modeling Site, Collaborative‐Interdisciplinary Cryospheric Experiment and 1998 North Water Polynya study over the period 1992 to 2002. The objectives are to investigate the seasonal co‐relationship of the SAR time‐series dataset with selected surface mass (bulk snow thickness) and climate state variables (surface temperature and albedo) measured in situ for the purpose of measuring the interannual variability of sea ice spring melt transitions and validating a time‐series SAR methodology for sea ice surface mass and climate state parameter estimation. We begin with a review of the salient processes required for our interpretation of time‐series microwave backscatter from land‐fast FYI. Our results suggest that time‐series SAR data can reliably measure the timing and duration of surface albedo transitions at daily to weekly time‐scales and at a spatial scales that are on the order of hundreds of metres. Snow thickness on FYI immediately prior to melt onset explains a statistically significant portion of the variability in timing of SAR‐detected melt onset to pond onset for SAR time‐series that are made up of more than 25 images. Our results also show that the funicular regime of snowmelt, resolved in time‐series SAR data at a temporal resolution of approximately 2·5 images per week, is not detectable for snow covers less than 25 cm in thickness. Copyright © 2007 John Wiley & Sons, Ltd.     

Application of synthetic aperture radar (SAR) imagery to volcano mapping in the humid tropics: a case study in East Java, Indonesia

 Volcanoes in humid tropical environments are frequently cloud covered, typically densely vegetated and rapidly eroded. These factors complicate field and laboratory studies and even the basic identification of potentially active volcanoes. Numerous previous studies have highlighted the potential value of radar remote sensing for volcanology in equatorial regions. Here, cloud- and vegetation-penetrating L_HH-band (λ≈24 cm) synthetic aperture radar (SAR) data from the Japanese Earth Resources Satellite (JERS-1) are used to investigate persistently active volcanoes and prehistoric calderas in East Java, Indonesia. The L_HH-band JERS-1 SAR produces high-spatial-resolution (18 m) imagery with relatively high incidence angle that highlights structures and topographic variations at or greater than the wavelength scale while minimising geometrical distortions such as layover and foreshortening. These images, along with Internet browse data derived from the Canadian RADARSAT mission, provide new evidence relating regional tectonics to volcanism throughout East Java. Volcanic events, such as caldera collapse at the Tengger caldera, appear to have been partly controlled by northwest-aligned faults related to intra-arc sedimentary basins. Similar regional controls appear important at historically active Lamongan volcano, which is encircled by numerous flank maars and cinder cones. A previously undocumented pyroclastic sheet and debris avalanche deposit from the Jambangan caldera complex is also manifested in the synoptic radar images. At the currently active Semeru volcano these data permit identification of recent pyroclastic flow and lahar deposits. Radar data therefore offer a valuable tool for mapping and hazard assessment at late Quaternary volcanoes. The criteria developed in the analysis here could be applied to other regions in the humid tropics. Received: 25 June 1998 / Accepted: 20 January 1999     

Soil moisture estimation using ERS 2 SAR data: a case study in the Solani River catchment/Estimation de l’humidité du sol grâce à des données ERS-2 SAR: étude de cas dans le bassin de la rivière Solani

Abstract

Abstract The information regarding spatial and temporal variation of soil moisture in a catchment is of utmost importance in hydrological, as well as many other studies. Point measurements from gravimetric and other methods for soil moisture determination are insufficient to understand the spatial behaviour of soil moisture in a region. Microwave remote sensing data from active sensors on board various satellites are increasingly being used to map spatial distribution of soil moisture within the 0–10 cm top surface. The northern part of India has a network of large rivers and canals and, therefore, spatial and temporal distribution of soil moisture in this region has a significant bearing on the hydrology of the region. In this paper, results on estimation of soil moisture from an ERS-2 SAR image in the catchment of the Solani River (a tributary to the River Ganga) in and around the town of Roorkee, India, have been presented. The radar backscatter coefficient for each pixel of the image has been modelled from the digital numbers of the SAR image. Gravimetric measurements have been made simultaneously during the satellite pass to determine the concurrent value of volumetric soil moisture at a large number of sample points within the satellite sweep area. The backscatter coefficient is found to vary from –30 dB to –42 dB for a variation in soil moisture from 30 to 75%. Regression analyses between volumetric soil moisture and both the digital numbers and backscatter coefficients were performed. Strong correlations between volumetric soil moisture and digital number were observed with R ² values of 0.84, 0.75 and 0.83 for bare soil, vegetative and combined surfaces, respectively. A similar trend was observed with the relationship between backscatter and volumetric soil moisture with R ² values of 0.60, 0.89 and 0.67 for bare soil, vegetative and combined surfaces, respectively. These results demonstrate the utilization of SAR data for estimation of spatial distribution of soil moisture in the region of the present study.     

Imprints of sub-glacial volcanic activity on a glacier surface—SAR study of Katla volcano,Iceland

The Katla central volcano, covered by the fourth largest Icelandic glacier Mýrdalsjökull, is among the most dangerous and active volcanoes in Iceland. Due to the ice cover, several indicators of its volcanic activity can only be identified indirectly. We analysed a total of 30 synthetic aperture radar (SAR) images with special focus on identifying circular and linear depressions in the glacier surface. Such features are indicative of sub-glacial geothermal heat sources and the adjacent sub-glacial tunnel (melt water drainage) system. The time series comprises images from five different SAR sensors (ERS-1, ERS-2, JERS-1/SAR, RADARSAT and ENVISAT-ASAR) covering a time period of 12 years, starting in 1994. Individual SAR scenes only partly map the glacier surface morphology due to the environmental influences on the SAR backscatter intensity. Thus, only surface features detectable in several SAR scenes at the same location were considered and merged to form an overall picture of the surface morphology of Mýrdalsjökull and its modification by sub-glacial volcanic activity between 1994 and 2006. Twenty permanent and 4 semi-permanent ice cauldrons could be identified on the surface of Mýrdalsjökull indicating geothermally active areas in the underlying caldera. An analysis of their size was not possible due to the indistinct outline in the SAR images. The spatial distribution of the geothermally active areas led to a new, piecemeal caldera model of Katla volcano. All cauldrons are connected to tunnel systems for melt water drainage. More than 100 km of the sub-glacial drainage system could be identified under the Mýrdalsjökull in the SAR time series. It has been found that the tunnel systems are not in agreement with estimated water divides. Our results allow improved assessment of areas of potential Jökulhlaup hazard accompanying a sub-glacial eruption.     

Hydraulics of interrill overland flow on rough,bare soil surfaces

A set of laboratory experiments on bare, rough soil surfaces was carried out to study the relationship between soil surface roughness and its hydraulic resistance. Existing models relating roughness coefficients to a measure of surface roughness did not predict the hydraulic resistance well for these surfaces. Therefore, a new model is developed to predict the hydraulic resistance of the surface, based on detailed surface roughness data. Roughness profiles perpendicular to the flow are used to calculate the wet cross‐sectional area and hydraulic radius given a certain water level. The algorithm of Savat is then applied to calculate the hydraulic resistance. The value for the equivalent roughness, which is used in the algorithm of Savat, could be predicted from the roughness profiles. Here, the tortuosity of the submerged part of the surface was used, which means that the calculated roughness depends on flow depth. The roughness increased with discharge, due to the fact that rougher parts of the surface became submerged at higher discharges. Therefore, a single measure of surface roughness (e.g. random roughness) is not sufficient to predict the hydraulic resistance. The proposed model allows the extension of the flow over the surface with increasing discharge to be taken into account, as well as the roughness within the submerged part of the surface. Therefore, the model is able to predict flow velocities reasonably well from discharge and roughness data only. Copyright © 2000 John Wiley & Sons, Ltd.     

Improving the representation of soil moisture by using a semi‐analytical infiltration model

Soil moisture is widely recognized as a fundamental variable governing the mass and energy fluxes between the land surface and the atmosphere. In this study, the soil moisture modelling at sub‐daily timescale is addressed by using an accurate representation of the infiltration component. For that, the semi‐analytical infiltration model proposed by Corradini et al. (1997) has been incorporated into a soil water balance model to simulate the evolution in time of surface and profile soil moisture. The performances of this new soil moisture model [soil water balance module‐semi‐analytical (SWBM‐SA)] are compared with those of a precedent version [SWBM‐Green–Ampt (GA)] where the GA approach was employed. Their capability to reproduce in situ soil moisture observations at three sites in Italy, Spain and France is analysed. Hourly observations of quality‐checked rainfall, temperature and soil moisture data for a 2‐year period are used for testing the modelling approaches. Specifically, different configurations for the calibration and validation of the models are adopted by varying a single parameter, that is, the saturated hydraulic conductivity. Results indicate that both SWBMs are able to reproduce satisfactorily the hourly soil moisture temporal pattern for the three sites with root mean square errors lower than 0.024 m³/m³ both in the calibration and validation periods. For all sites, the SWBM‐SA model outperforms the SWBM‐GA with an average reduction of the root mean square error of ~20%. Specifically, the higher improvement is observed for the French site for which in situ observations are measured at 30 cm depth, and this is attributed to the capability of the SA infiltration model to simulate the time evolution of the whole soil moisture profile. The reasonable models performance coupled with the need to calibrate only a single parameter makes them useful tools for soil moisture simulation in different regions worldwide, also in scarcely gauged areas. Copyright © 2013 John Wiley & Sons, Ltd.     

The retrieval of two-dimensional distribution of the earth''''s surface aerodynamic roughness using SAR image and TM thermal infrared image

The research for the land surface fluxes has madea quiet great progress for its breakthroughs in the fieldof regional or global interactions between land surfaceand atmosphere. However, many remote sensing mod-els for estimating the land surface fluxes need the pa-rameters of surface momentum, heat, resistance ofwater vapor at a referenced height, which are the func-tion of aerodynamic surface roughness zad. It hasbeen validated that the retrieval of the land surfacefluxes is very sensitive to…     

Satellite remote sensing of river inundation area,stage, and discharge: a review

The growing availability of multi-temporal satellite data has increased opportunities for monitoring large rivers from space. A variety of passive and active sensors operating in the visible and microwave range are currently operating, or planned, which can estimate inundation area and delineate flood boundaries. Radar altimeters show great promise for directly measuring stage variation in large rivers. It also appears to be possible to obtain estimates of river discharge from space, using ground measurements and satellite data to construct empirical curves that relate water surface area to discharge. Extrapolation of these curves to ungauged sites may be possible for the special case of braided rivers. Where clouds, trees and floating vegetation do not obscure the water surface, high-resolution visible/infrared sensors provide good delineation of inundated areas. Synthetic aperture radar (SAR) sensors can penetrate clouds and can also detect standing water through emergent aquatic plants and forest canopies. However, multiple frequencies and polarizations are required for optimal discrimination of various inundated vegetation cover types. Existing single-polarization, fixed-frequency SARs are not sufficient for mapping inundation area in all riverine environments. In the absence of a space-borne multi-parameter SAR, a synergistic approach using single-frequency, fixed-polarization SAR and visible/infrared data will provide the best results over densely vegetated river floodplains. © 1997 John Wiley & Sons, Ltd.     

A top–down soil moisture and sap flux sampling design of a rain–snow transition mountain watershed

This paper presents a top–down approach for soil moisture and sap flux sampling design with the goal of understanding ecohydrologic response to interannual climate variation in the rain–snow transition watersheds. The design is based on a priori estimates of soil moisture and transpiration patterns using a physical distributed model, Regional Hydro‐Ecologic Simulation System (RHESSys). RHESSys was initially calibrated with existing snow depth and streamflow data. Calibrated model estimates of seasonal trajectories of snowmelt, root‐zone soil moisture storage, and transpiration were used to develop five hydrologic similarity indicators and map these at (30 m) patch scale across the study watershed. The partitioning around medoids‐clustering algorithm was then used to define six distinctive spatially explicit clusters based on the five hydrologic similarity indictors. A representative site within each cluster was identified for sampling. For each site, soil moisture sensors were installed at the 30‐ and 90‐cm depths and at the five soil pits and a sap flux sensor at the averaged‐size white fir tree for each site. The model‐based cluster analysis suggests that the elevation gradient and topographically driven flow drainage patterns are the dominant drivers of spatial patterns of soil moisture and transpiration. The comparison of model‐based calculated hydrological similarity indicators with measured‐data‐based values shows that spatial patterns of field‐sampled soil moisture data typically fell within uncertainty bounds of model‐based estimates for each cluster. There were however several notable exceptions. The model failed to capture the soil moisture and sap flux dynamics in a riparian zone site and in a site where lateral subsurface flow may not follow surface topography. Results highlight the utility of using a hypothesis driven sampling strategy, based on a physically based model, for efficiently providing new information that can drive both future measurements and strategic refinements to model inputs, parameters, or structure that might reduce these errors. Future research will focus on strategies for using of finer scale representations of microclimate, topography, vegetation, and soil properties to improve models.     

Characterization of field scale soil variability using remotely and proximally sensed data and response surface method

Soil salinization of the reclaimed tidelands is problematic. Therefore, there is a need to characterize the spatial variability of soil salinity associated with soil moisture and other soil properties across the reclaimed tidelands. One approach is the use of easily-acquired ancillary data as surrogates for the arduous conventional soil sampling. In a reclaimed coastal tideland in the south of Hangzhou Gulf, backscattering coefficient (σ⁰) from remotely sensed ALOS/PALSAR radar imagery (HH polarization mode) and apparent soil electrical conductivity (ECa) from a proximally sensed EM38 were used to indicate the spatial distribution of soil moisture and salinity, respectively. After that, response surface methodology (RSM) was employed to determine an optimal set of 12 soil samples using spatially referenced σ⁰ and ECa data. Spatial distributions of three soil chemical properties [i.e. soil organic matter (SOM), available nitrogen (AN), and available potassium (AK)] were predicted using inverse distance weighted method based on the 12 samples and were then compared with the predictions generated using 42 samples obtained from a conventional grid sampling scheme. It was concluded that combination of radar imagery and EM induction data can delineate the spatial variability of two key soil properties (i.e. moisture and salinity) across the study area. Besides, RSM-based sampling using radar imagery and EM induction data was highly effective in characterizing the spatial variability of SOM, AN and AK, compared with the conventional grid sampling. This new approach may be used to assist site specific management in precision agriculture.     

Soil moisture monitoring on a steep hillside

In this study, a soil monitoring system for a hillslope with steep relief and shallow soil depth was designed and installed to represent efficiently the spatial and temporal features of soil moisture. The study was conducted on a mountainous hillslope of the Sulmachun catchment (northeastern South Korea). The positions of soil moisture sensors were determined through a sequential procedure including intensive geomorphologic surveying of the study area, surface and subsurface terrain analysis, and inverse surveying. Using 26 sensors, soil moisture data from 11 locations were measured and recorded at hourly intervals over 380 h from 6 to 22 November 2003. Soil moisture response patterns were captured for a few consecutive rainfall events. The monitoring results are discussed in the context of soil moisture variations with terrain attributes. The immediate recharge and fast recession after a peak are the primary features of soil moisture in the upper zone. Stability and significant storage increase are distinct characteristics of soil moisture in the buffer zone and the flow path zone respectively. Spatial distribution of temporal soil moisture variations can be characterized in terms of recession, stability and recharge depending upon the topographic classification of a hillslope for this approach. Copyright © 2007 John Wiley & Sons, Ltd.     

L和C波段雷达干涉数据矿区地面沉降监测能力分析

雷达成像的波长、入射角、地面分辨率等参数严重影响着SAR差分干涉测量地面沉降的监测能力和精度,论文通过理论推导和矿区实际沉降差分干涉相位模拟,从监测到的最大沉降梯度和沉降量、保相能力、对微小沉降的敏感程度等方面对L和 C波段雷达干涉数据的矿区地面沉降监测能力进行分析;精化双轨D-InSAR数据处理的流程、方法和相应参数,使用ALOS PALSAR和ENVISAT ASAR数据获取济宁某矿区2009年12月到2010年02月期间更为精确的矿区地面沉降结果,并对沉降结果进行详细比较和系统分析.理论推导、相位模拟和真实数据实验都表明,相对于C波段的雷达干涉数据而言,L波段雷达干涉数据具有较强的保相能力,能够更好地降低失相干和相位不连续性的影响,更容易监测到沉降梯度和沉降量较大的矿区地面沉降,但对微小矿区地面沉降的敏感程度较低.     

Spatio‐temporal distribution of near‐surface and root zone soil moisture at the catchment scale

Soil moisture is highly variable both spatially and temporally. It is widely recognized that improving the knowledge and understanding of soil moisture and the processes underpinning its spatial and temporal distribution is critical. This paper addresses the relationship between near‐surface and root zone soil moisture, the way in which they vary spatially and temporally, and the effect of sampling design for determining catchment scale soil moisture dynamics. In this study, catchment scale near‐surface (0–50 mm) and root zone (0–300 mm) soil moisture were monitored over a four‐week period. Measurements of near‐surface soil moisture were recorded at various resolutions, and near‐surface and root zone soil moisture data were also monitored continuously within a network of recording sensors. Catchment average near‐surface soil moisture derived from detailed spatial measurements and continuous observations at fixed points were found to be significantly correlated (r² = 0·96; P = 0·0063; n = 4). Root zone soil moisture was also found to be highly correlated with catchment average near‐surface, continuously monitored (r² = 0·81; P < 0·0001; n = 26) and with detailed spatial measurements of near‐surface soil moisture (r² = 0·84). The weaker relationship observed between near‐surface and root zone soil moisture is considered to be caused by the different responses to rainfall and the different factors controlling soil moisture for the soil depths of 0–50 mm and 0–300 mm. Aspect is considered to be the main factor influencing the spatial and temporal distribution of near‐surface soil moisture, while topography and soil type are considered important for root zone soil moisture. The ability of a limited number of monitoring stations to provide accurate estimates of catchment scale average soil moisture for both near‐surface and root zone is thus demonstrated, as opposed to high resolution spatial measurements. Similarly, the use of near‐surface soil moisture measurements to obtain a reliable estimate of deeper soil moisture levels at the small catchment scale was demonstrated. Copyright © 2007 John Wiley & Sons, Ltd.     

Radar altimetry backscattering signatures at Ka,Ku, C,and S bands over West Africa

This study presents a comprehensive comparison of radar altimetry signatures at Ka-, Ku-, C-, and S-bands using SARAL, ENVISAT and Jason-2 data over the major bioclimatic zones, soil and vegetation types encountered in West-Africa, with an emphasis on the new information at Ka-band provided by the recently launched SARAL–Altika mission. Spatio-temporal variations of the radar altimetry responses were related to changes in surface roughness, land cover and soil wetness. Analysis of time series of backscattering coefficients along the West African bioclimatic gradient shows that radar echoes at nadir incidence are well correlated to soil moisture in semi-arid savannah environments. Radar altimeters are able to detect the presence of water even under a dense canopy cover at all frequencies. But only measurements at Ka-band are able to penetrate underneath the canopy of non-inundated tropical evergreen forests.