Monitoring soil moisture dynamics using satellite imaging radar in northeastern Jordan

Tansey, Kevin; Millington, Andrew; Battikhi, A. M.; White, Kevin

doi:10.1016/s0143-6228(99)00009-0

Cited by 20 publications

(17 citation statements)

References 16 publications

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“…Indeed, the influence of the green vegetation can be either neglected or simply taken into account at low incidence angles and for low vegetation density (Leaf Area Index -LAI -<2 m 2 m −2 ) as proposed in Tansey et al, 1999 andMoran et al, 2000. At higher incidence angles, the vegetation effects on the radar signal can be corrected using the angular properties of the backscattering coefficient (Wagner et al, 2000;Baup et al, 2007a).…”

Section: Introductionmentioning

confidence: 99%

Mapping surface soil moisture over the Gourma mesoscale site (Mali) by using ENVISAT ASAR data

Baup

Mougin

Rosnay

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. The potentialities of ENVISAT ASAR (Advanced Synthetic Aperture Radar) multi-angle data for mapping surface soil moisture (SSM) in Sahelian rangelands are investigated at medium scale (30 000 km 2 ). The Wide Swath data are selected to take advantage of their high temporal repetitivity (about 8 days at the considered scale) associated to a moderate spatial resolution (150 m). In the continuity of previous studies conducted at a local scale in the same region, SSM maps are here processed over the whole AMMA Gourma mesoscale site at 1 km resolution scale. Overall, the generated maps are found to be in good agreement with field data, EPSAT-SG (Estimation des Pluies par SATellite -Second Generation) rainfall estimates and ERS (European Remote Sensing) Wind Scatterometer (WSC) SSM products. The present study shows that the spatial pattern of SSM can be realistically estimated at a kilometric scale. The resulting SSM maps are expected to provide valuable information for initialisation of land surface models and the estimation of the spatial distribution of radiative fluxes. Particularly, SSM maps could help to desaggregate low-resolution products such as those derived from WSC data.

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Section: Introductionmentioning

confidence: 99%

Mapping surface soil moisture over the Gourma mesoscale site (Mali) by using ENVISAT ASAR data

Baup

Mougin

Rosnay

et al. 2011

Hydrol. Earth Syst. Sci.

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“…In nonforested areas, remote sensing techniques offer the ability to quantify internal state variables in a spatially distributed manner (Tansey et al, 1999). However, in forested terrain, most of the techniques available are limited to ground-based measurements of groundwater and soil water owing to vegetation interference with space and airborne electromagnetic signals.…”

Section: Introductionmentioning

confidence: 99%

A new tool for hillslope hydrologists: spatially distributed groundwater level and soilwater content measured using electromagnetic induction

Sherlock¹,

McDonnell

2003

Hydrological Processes

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Abstract:New methods for obtaining and quantifying spatially distributed subsurface moisture are a high research priority in process hydrology. We use simple linear regression analyses to compare terrain electrical conductivity measurements (EC) derived from multiple electromagnetic induction (EMI) frequencies to a distributed grid of water-table depth and soil-moisture measurements in a highly instrumented 50 by 50 m hillslope in Putnam County, New York. Two null hypotheses were tested: H0 1 , there is no relationship between water table depth and EC; H0 2 , there is no relationship between soil moisture levels and EC. We reject both these hypotheses. Regression analysis indicates that EC measurements from the low frequency EM31 meter with a vertical dipole orientation could explain over 80% of the variation in water-table depth across the test hillslope. Despite zeroing and sensitivity problems encountered with the high frequency EM38, EC measurements could explain over 70% of the gravimetrically determined soil-moisture variance. The use of simple moisture retrieval algorithms, which combined EC measurements from the EM31 and EM38 meters in both their vertical and horizontal orientations, helped increase the r 2 coefficients slightly. This first hillslope hydrological analysis of EMI technology in this way suggests that it may be a promising method for the collection of a large number of distributed soilwater and groundwater depth measurements with a reasonable degree of accuracy.

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“…This hot spot "indicates where 26 the routine monitoring of soil moisture, with both ground-based 27 and space-based systems, will yield the greatest return in boreal 28 summer seasonal forecasting". Monitoring the spatial and 29 temporal variability of soil moisture is also critical for 30 understanding soil-vegetation-atmosphere interactions and to 31 Tansey et al, 1999). In terms of 77 dominantly vertically-orientated herbaceous vegetation, the use 78 of the HH polarization is expected to improve the SSM esti-79 mation from space due to the corresponding larger SSM 80 sensitivity, especially at low incidence angle (Ulaby, 1975).…”

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confidence: 99%

Surface soil moisture estimation over the AMMA Sahelian site in Mali using ENVISAT/ASAR data

Baup

Mougin

Rosnay

et al. 2007

Remote Sensing of Environment

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9This paper focuses on different methods for estimating soil moisture in a Sahelian environment by comparing ENVISAT/ASAR and ground data 10 at the same spatial scale. The analysis is restricted to Wide Swath data in order to take advantage of their high temporal repetitivity (about 3-4 days) 11 corresponding to a moderate spatial resolution (150 m). On the one hand, emphasis is put on the characterization of Surface Soil Moisture (SSM) at a 12 spatial scale compatible with the derivation of the backscattering coefficients, and a transfer function is developed for up-scaling local measurements 13 to the 1 km scale. On the other hand, three different approaches are used to normalize the angular variation of the observed backscattering coefficients. 14 The results show a strong linear relationship between the HH normalized backscattering coefficients and SSM. The best result is obtained when 15 restricting the ASAR data to low incidence angles and by taking into account vegetation effects using multi-angular radar data. For this case, the rms 16 error of the SSM retrieval is 2.8%. These results highlight the capabilities of the ASAR instrument to monitor SSM in a semiarid environment. 17

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Monitoring soil moisture dynamics using satellite imaging radar in northeastern Jordan

Cited by 20 publications

References 16 publications

Mapping surface soil moisture over the Gourma mesoscale site (Mali) by using ENVISAT ASAR data

Mapping surface soil moisture over the Gourma mesoscale site (Mali) by using ENVISAT ASAR data

A new tool for hillslope hydrologists: spatially distributed groundwater level and soilwater content measured using electromagnetic induction

Surface soil moisture estimation over the AMMA Sahelian site in Mali using ENVISAT/ASAR data

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