Remote sensing in hydrology

Schmugge, Thomas J.; Kustas, William P.; Ritchie, Jerry C.; Jackson, Thomas J.; Rango, A.

doi:10.1016/s0309-1708(02)00065-9

Cited by 480 publications

(291 citation statements)

References 114 publications

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“…Based on our previous paper (Kong and Dorling, 2008), it may also be possible to integrate soil moisture estimation from satellite images into the model. The satellite data can offer the possibility to improve the accuracy and resolution of model results by being used as initial conditions or adjusting the model to minimize the errors during the model execution through data assimilation (Giacomelli et al, 1995;Schmugge et al, 2002), with the resulting prospect of enhanced operational applications in hydro-and agricultural-meteorology.…”

Section: Resultsmentioning

confidence: 99%

Soil moisture modelling and validation at an agricultural site in Norfolk using the Met Office surface exchange scheme (MOSES)

Kong

Dorling

Smith

2011

Meteorological Applications

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ABSTRACT:There is an urgent need for improved soil moisture measurements and modelling due to the relevance of surface moisture to the local energy balance (and hence, for example, air temperature forecasting) to flood prediction and to a range of agricultural applications including plant stress and accessibility of heavy machinery onto the land. The UK Met Office Surface Exchanges Scheme (MOSES) represents land surface processes in the Met Office's Unified Model (MetUM). In this research, MOSES was used to simulate soil moisture over an agricultural site in Norfolk, United Kingdom and validate the model estimations against ground truth soil moisture measurements. Two versions of the MOSES model have been used: the Stand-Alone MOSES (for site-specific modelling) and the Nimrod nowcasting products (5 km grid data) based on MOSES Probability Distributed Moisture (MOSES-PDM). The validation results show that both versions of MOSES perform well in soil moisture estimations in general. However, the MOSES model produces a relatively large seasonal bias in the late summer and autumn seasons and the Nimrod-MOSES-PDM tends to underestimate soil moisture in general. A model sensitivity study shows that the soil parameters are most likely to contribute to the errors. In addition, the MOSES model treats the vegetation fraction as a constant throughout a year over agricultural fields, which was found partly to cause the seasonal bias. Recommendations on how to improve MOSES soil moisture estimation are discussed, as are the likely application benefits.

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Section: Resultsmentioning

confidence: 99%

Soil moisture modelling and validation at an agricultural site in Norfolk using the Met Office surface exchange scheme (MOSES)

Kong

Dorling

Smith

2011

Meteorological Applications

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“…To date, there is no spaceborne sensor measuring the microwave emission of the soil surface at this frequency (Schmugge et al, 2002), although several new programs are scheduled: the European Space Agency passive L-band Soil Moisture and Ocean Salinity (ESA-SMOS) mission (2007 launch) and the U.S. National Aeronautics and Space Administration active/passive L-band HYDROSpheric states (NASA-HYDROS) mission (2009 launch) are two examples. Therefore, there is an urgent need to adapt other microwave sensors to this application (Jackson 1997, Paloscia et al, 2001Jackson et al, 2002;Walker and Houser, 2004).…”

Section: The Advanced Microwave Sounding Unit (Amsu)mentioning

confidence: 99%

“…The main reason is because, unfortunately, the estimate of soil moisture by means of microwave radiances is strongly affected by surface roughness and vegetation cover, whose contributions may perturb the measured signal, often masking the effects induced by soil moisture changes (Choudhury et al, 1979;Wang et al, 1983;Jackson and Schmugge, 1989;Prigent et al, 1997;Schmugge et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Monitoring soil wetness variations by means of satellite passive microwave observations: the HYDROPTIMET study cases

Lacava

Greco

Leo

et al. 2005

Nat. Hazards Earth Syst. Sci.

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Abstract. Soil moisture is an important component of the hydrological cycle. In the framework of modern flood warning systems, the knowledge of soil moisture is crucial, due to the influence on the soil response in terms of infiltrationrunoff. Precipitation-runoff processes, in fact, are related to catchment's hydrological conditions before the precipitation. Thus, an estimation of these conditions is of significant importance to improve the reliability of flood warning systems. Combining such information with other weatherrelated satellite products (i.e. rain rate estimation) might represent a useful exercise in order to improve our capability to handle (and possibly mitigate or prevent) hydro-geological hazards.Remote sensing, in the last few years, has supported several techniques for soil moisture/wetness monitoring. Most of the satellite-based techniques use microwave data, thanks to the all-weather and all-time capability of these data, as well as to their high sensitivity to water content in the soil. On the other hand, microwave data are unfortunately highly affected by the presence of surface roughness or vegetation coverage within the instantaneous satellite field of view (IFOV). Those problems, consequently, strongly limit the efficiency and the reliability of traditional satellite techniques.Recently, using data coming from AMSU (Advanced Microwave Sounding Unit), flying aboard NOAA (National Oceanic and Atmospheric Administration) satellites, a new methodology for soil wetness estimation has been proposed. The proposed index, called Soil Wetness Variation Index (SWVI), developed by a multi-temporal analysis of AMSU records, seems able to reduce the problems related to vegetation and/or roughness effects. Such an approach has been tested, with promising results, on the analysis of some flooding events which occurred in Europe in the past.Correspondence to: N. Pergola (pergola@imaa.cnr.it)In this study, results achieved for the HYDROPTIMET test cases will be analysed and discussed in detail. This analysis allows us to evaluate the reliability and the efficiency of the proposed technique in identifying different amounts of soil wetness variations in different observational conditions. In particular, the proposed indicator was able to document the actual effects of meteorological events, in terms of space-time evolution of soil wetness changes, for all the analysed HYDROPTIMET test cases. Moreover, in some circumstances, the SWVI was able to identify the presence of a sort of "early" signal in terms of soil wetness variations, which may be regarded as a timely indication of an anomalous value of soil water content. This evidence suggests the opportunity to use such an index in the pre-operational phases of the modern flood warning systems, in order to improve their forecast capabilities and their reliability.

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“…6 Dielectric methods have become a standard approach for determining soil water content from both in situ measurements and remote sensing. 7,8 Recently, the use of ground penetrating radar (GPR) has been applied to determining earth structure. 9 However, researchers are increasingly recognizing that the soil or sediment microstructure plays an important role in determining the effective dielectric response.…”

Section: Introductionmentioning

confidence: 99%

“…Exact theory and multipole simulations both show that simplecubic (sc) lattices and random packings have higher permittivities than body-centered-cubic (bcc), face-centered-cubic (fcc), and hexagonal-close-packed (hcp) lattices for packings of equal volume fraction. 43 Since sc lattices and maximally random packings have a lower coordination number (6) than bcc (8), fcc (12), and hcp (12) lattices, the particles in these structures will be 11% (compared to fcc and hcp) to 17% (compared to bcc) closer to their nearest neighbors for equal volume-fraction packings. This creates a more open, foam-like structure for the sc and random structures, with larger interstices and void spaces than the bcc, fcc, or hcp lattices.…”

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confidence: 99%

Effects of aggregation on the permittivity of random media containing monodisperse spheres

Doyle

Tew

Jain

et al. 2009

Journal of Applied Physics

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The NERC and CEH trade marks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. AbstractNumerical simulations were used to calculate the effective permittivities of three-dimensional random particle suspensions containing up to 2440 particles and exhibiting two types of particle aggregation. The particles were modeled as 200-µm spheres that were aggregated into either large spherical clusters or into foam-type microstructures with large spherical voids. Multiple scattering of 0.01-10.0 GHz electromagnetic fields was simulated using a first-principles iterative multipole approach with matrix and particle permittivities of 1.0 and 8.5, respectively.The computational results showed both significant and highly significant trends. Aggregation 2 into spherical clusters decreased the effective permittivity by up to 3.2 ± 0.2%, whereas aggregation into foam-type microstructures increased the effective permittivity by up to 3.0 ± 1.6%. The effective permittivity trends exhibited little change with frequency. These results were compared to effective medium approximations that predicted higher permittivities than those from the simulations and showed opposite trends for cluster aggregation. Three theories are proposed to explain the simulation results. The first theory invokes a waveguide-like mechanism. The simulations indicate that the wave fields propagate more through the continuous paths of greater or lesser particle density created by aggregation, rather than through the isolated particle clusters or large voids. This quasi-continuous phase, or quasi-matrix, therefore behaves like a random waveguide structure in the material. A second theory is proposed where the quasicontinuous phase governs the behavior of the system by a percolation-like process. In this theory, the multipole interactions are modeled as the percolation of virtual charges tunneling from one particle to another. A third mechanism for the permittivity changes is also proposed involving collective polarization effects associated with the particle clusters or large voids. The simulation results challenge the general applicability of the quasistatic limit for heterogeneous media by showing how microstructural changes much smaller than the electromagnetic wavelength can alter the effective permittivity by a statistically significant degree. The results also provide a quantitative indication of the effects of aggregation and hierarchical microstructures on the electromagnetic properties of random media, and have application to the remote and in situ sensing of soils, the rational design and nondestructive evaluation of composites, and the study of biological tissues and other random materials.3

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Remote sensing in hydrology

Cited by 480 publications

References 114 publications

Soil moisture modelling and validation at an agricultural site in Norfolk using the Met Office surface exchange scheme (MOSES)

Soil moisture modelling and validation at an agricultural site in Norfolk using the Met Office surface exchange scheme (MOSES)

Monitoring soil wetness variations by means of satellite passive microwave observations: the HYDROPTIMET study cases

Effects of aggregation on the permittivity of random media containing monodisperse spheres

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