A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data

Wagner, Wolfgang; Lemoine, Guido; Rott, Helmut

doi:10.1016/s0034-4257(99)00036-x

Cited by 1,048 publications

(980 citation statements)

References 17 publications

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“…This model was developed by VU University Amsterdam in collaboration with NASA and uses a radiative transfer model to convert the observed brightness temperatures into volumetric soil moisture values. The ASCAT soil moisture product was developed by Vienna University of Technology and is based on a change detection algorithm [Wagner et al, 1999]. This product describes soil moisture in degrees of saturation.…”

Section: Methods Summarymentioning

confidence: 99%

Afternoon rain more likely over drier soils

Taylor

Jeu

Guichard

et al. 2012

Nature

519

520

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(2012), Afternoon rain more likely over drier soils, Nature, 489(7416), 423-426 (10.1038/nature11377) Land surface properties influence the partition of radiative energy between latent and sensible heat fluxes during daytime. A climatically important process during dry periods is the impact of soil water deficit on limiting evapotranspiration, leading to increased surface heating of the lower atmosphere 1-2 . Soil moisture can influence the development of convective storms through modification of low level atmospheric temperature and humidity 1,3 , which in turn feeds back on soil moisture. There is considerable uncertainty in how soil moisture affects convective storms across the world, due to both a lack of observational evidence and uncertainty in large-scale models 4 . Here we present the first global-scale observational analysis of the coupling between soil moisture and precipitation. This shows that across all six continents studied, afternoon rain falls preferentially over soils which are relatively dry compared to the surrounding area. The signal emerges most clearly in the observations over semi-arid regions, where surface fluxes are sensitive to soil moisture and convective events are frequent. We find no evidence in our analysis of a positive feedback i.e. preference for rain over wetter soils, at the 50-100 km scale studied. In contrast, our analysis of six state-of-the-art global weather and climate models demonstrates the dominance of a positive feedback of soil moisture on simulated precipitation. These results suggest a fundamental weakness in the sensitivity of convection to the land surface in large-scale models, which may contribute to excessive simulated droughts.Soil moisture influences precipitation on a range of time and space scales 5 . In drought-affected continental regions, weak evapo-transpiration leads to reduced atmospheric moisture content over a period of days, potentially suppressing subsequent precipitation 6 . When soil moisture anomalies are extensive, surface-induced perturbations to the atmospheric heat budget may modify synoptic scale circulations 2 , in turn affecting moisture advection from the oceans 7 . On smaller scales, the development of convective clouds and precipitation can be influenced by local surface fluxes over the course of the day 1,3 . Theoretical considerations [8][9] suggest that, in an undisturbed atmosphere, the likelihood and sign of a surface feedback will be determined by the atmospheric profiles of temperature and humidity. Thus one might expect regional variations in the strength and sign of convective sensitivity to soil moisture [10][11] . Mesoscale soil moisture variability can also influence the feedback via the development of daytime circulations 12 , which provide additional convergence to trigger convection [13][14] .There have been a number of studies examining the impact of the land surface on observed rainfall in different regions of the world. Analyses in Illinois 15 and West Africa 16 have indicated positive correlations between ...

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Section: Methods Summarymentioning

confidence: 99%

Afternoon rain more likely over drier soils

Taylor

Jeu

Guichard

et al. 2012

Nature

519

520

View full text Add to dashboard Cite

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“…The comparison between the modelled and the remotely sensed soil moisture estimates was done using the computation of the Soil Water Index ( SWI) which is the relative soil moisture throughout the soil depth. As the satellite only provides soil moisture for the topsoil layer (first 5 cm), a conceptual infiltration model developed by Wagner et al (1999c) was applied to the remotely sensed surface soil moisture estimates in order to estimate an SWI. The comparison between the modelled and remotely sensed SWI was shown to be good with regression coefficient varying between 0.678 and 0.923.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the surface soil moisture available in the global ERS soil moisture product, a Soil Water Index (SWI) is provided that aims to estimate the soil moisture profile in the soil horizon from the ERS product. The method used here to estimate SWI was proposed by Wagner et al (1999c). It is a simple conceptual infiltration model based on an exponential filter, temporally smoothing the signal of the (instantaneously estimated) relative surface soil moisture to give the Soil Water Index, SWI:…”

Section: Comparison Of Remotely Sensed and Modelled Soil Moisturementioning

confidence: 99%

“…To maintain the crucial independence of the physically based approach of TOPKAPI and the remotely sensed soil moisture estimates, it was decided not to refine the estimation of the parameter T for the particular study area by using the soil data. Thus the value of T =20 days, suggested by Wagner et al (1999c) as an average value, was retained. More detailed discussions of the SWI method can be found in Ceballos et al (2005) and Pellarin et al (2006).…”

Section: Comparison Of Remotely Sensed and Modelled Soil Moisturementioning

confidence: 99%

“…The standard assumption for the thin remotely sensed surface layer (m s ) is that minimum backscatter represents a completely dry soil and maximum backscatter water saturated soil (Wagner et al, 1999b). For the soil profile (SWI) minimum backscatter is in general related to a soil with water content at wilting point and maximum backscatter to a soil with a soil moisture content halfway between field capacity and total water capacity (Wagner et al, 1999c). The validity of these assumptions depends on the weather conditions during the reference period and the regionally varying climate.…”

Section: Uncertainties Associated With Remote Sensingmentioning

confidence: 99%

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Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

Vischel

Pegram²,

Sinclair³

et al. 2008

Hydrol. Earth Syst. Sci.

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Abstract. The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km 2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R 2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.

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Estimation of Surface Soil Moisture Using Microwave Sensors

Jackson

2005

Encyclopedia of Hydrological Sciences

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Soil moisture has been particularly difficult to measure and map using conventional ground‐based point sampling. Microwave remote sensing–based soil moisture retrieval has been demonstrated using tower and aircraft instruments. The translation of this approach to satellites and the implementation in hydrologic applications has been limited by both the technology and the satellite systems that were available. Recent developments in both science and associated technologies now make the exploitation of the microwave region for soil moisture mapping feasible. A number of new and potential satellite missions are reviewed and evaluated for soil moisture applications.

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A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data

Cited by 1,048 publications

References 17 publications

Afternoon rain more likely over drier soils

Afternoon rain more likely over drier soils

Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

Estimation of Surface Soil Moisture Using Microwave Sensors

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