Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations

Hallikainen, M.; Ulaby, F. T.; Dobson, M.C.; El-Rayes, Mohamed A.; Wu, Lil-Kun

doi:10.1109/tgrs.1985.289497

Cited by 1,077 publications

(512 citation statements)

References 8 publications

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“…According to Dobson et al [40] and Hallikainen et al [65], we investigated the sensitivity of the retrieval algorithm for soil texture and soil temperature, which were identified as the most important physical soil parameters for this approach. In addition, we conducted a sensitivity assessment of the soil density because this parameter exhibited a rather large variability in our soil sampling.…”

Section: Sensitivity Analysis Of Gps Soil Moisture Measurementsmentioning

confidence: 99%

“…Regarding different soil textures with different sand, silt and clay contents, the same SM values are based on different values of the complex permittivity of moist soil [40,65]. The soil texture is mainly represented by the physical soil parameter A s , which was set to the value of 49 m 2¨g´1 for the observed soil at the DWD test site in Munich.…”

Section: Sensitivity Analysis Of Gps Soil Moisture Measurementsmentioning

confidence: 99%

“…According to these findings, great effort should be taken to obtain reliable soil texture measurements in the laboratory. In general, the GPS SM retrieval is based on the complex permittivity of water, which is temperature dependent in the microwave range [40,65] (see also Table 2). For this study, we used separate soil temperature measurements to correctly adapt the Dobson four-component dielectric mixing model.…”

Section: Sensitivity Analysis Of Gps Soil Moisture Measurementsmentioning

confidence: 99%

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Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Koch

Schlenz

Prasch

et al. 2016

Water

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Soil moisture (SM) is a highly relevant variable for agriculture, the emergence of floods and a key variable in the global energy and water cycle. In the last years, several satellite missions have been launched especially to derive large-scale products of the SM dynamics on the Earth. However, in situ validation data are often scarce. We developed a new method to retrieve SM of bare soil from measurements of low-cost GPS (Global Positioning System) sensors that receive the freely available GPS L1-band signals. The experimental setup of three GPS sensors was installed at a bare soil field at the German Weather Service (DWD) in Munich for almost 1.5 years. Two GPS antennas were installed within the soil column at a depth of 10 cm and one above the soil. SM was successfully retrieved based on GPS signal strength losses through the integral soil volume. The results show high agreement with measured and modelled SM validation data. Due to its non-destructive, cheap and low power setup, GPS sensor networks could also be used for potential applications in remote areas, aiming to serve as satellite validation data and to support the fields of agriculture, water supply, flood forecasting and climate change.

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Section: Sensitivity Analysis Of Gps Soil Moisture Measurementsmentioning

confidence: 99%

Section: Sensitivity Analysis Of Gps Soil Moisture Measurementsmentioning

confidence: 99%

Section: Sensitivity Analysis Of Gps Soil Moisture Measurementsmentioning

confidence: 99%

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Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Koch

Schlenz

Prasch

et al. 2016

Water

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“…Since the gravimetric method cannot be used for repetitive measurements at exactly the same position or the same target, the volumetric soil moisture will be measured by a Time Domain Reflectometry system (TDR). After the determination of the volumetric soil moisture, the dielectric constant of the soil can be derived using some empirical or semi-empirical model: the Topp model, the Dobson-Peplinsky Model [14,15], and the Hallikainen model [16]. From the expressions of these three models [40], the knowledge of the soil particle compositions is essential at least for the two last models.…”

Section: Soil Moisturementioning

confidence: 99%

“…The Hallikainen model [16], relates the dielectric constant to the frequency, the volumetric moisture content and the percentage of sand and clay contained in the soil. It has been used to prove the relationship between the soil moisture and penetration depth [16], Because of the non-linear relationship between soil moisture and penetration depth, (see Figure 23), a measurement of a change in penetration depth cannot be directly converted to a change in soil moisture unless one of the soil moisture values is known a priori or if some linearizing assumptions are made. For example, a measured displacement of 5 mm could ambiguously mean a change in soil moisture from 1-2% or from 10-17%.…”

Section: Theorymentioning

confidence: 99%

Surface Parameter Estimation Using Bistatic Polarimetric X-Band Measurements

Khadhra

2012

PIER B

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Abstract-The main purpose of this paper is to separately estimate the important surface parameters (soil moisture and roughness) by using full polarimetric bistatic measurements. The results provide a basis for new satellite application of future bistatic measurement systems such as the TanDEM-X satellite mission.Initially, bistatic X-band measurements, which have been recorded in the Bistatic Measurement Facility (BMF) at the DLR Oberpfaffenhofen, Microwaves and Radar Institute, will be presented. The bistatic measurement sets are composed of soils with different well-known statistical roughness scales and different moistures. The BMF has been calibrated using the Isolated Antenna Calibration Technique (IACT). The validation of the calibration was achieved by measuring the reflectivity of fresh water. In the second part, the assessment of the surface parameters (soil moisture and surface roughness) using the well calibrated data introduced in the former related part, will be detailed. The validation of the specular algorithm by estimating the soil moisture of two surfaces with different roughness scales will be reported. Additionally, a new technique using the coherent term of the Integral Equation Method (IEM) to estimate the soil roughness will be presented, as well as the sensitivity of phase and reflectivity with regard to moisture variation and therefore the penetration depth was evaluated. Current results demonstrate a non-linear relationship

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Characterization of wet soils in the 2-18 GHz frequency range

Storme

Huynen

Vorst

1999

Microw. Opt. Technol. Lett.

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In order to evaluate the performances of microwave technologies in land‐mine detection, the electrical properties of soils have been extensively evaluated from 2 to 18 GHz both theoretically and experimentally, using a new transmission method, as a function of soil type and water content. This results in a new model for the microwave permittivity of soils, valid over a wide frequency range. ©1999 John Wiley & Sons, Inc. Microwave Opt Technol Lett 21: 333–335, 1999.

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Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations

Cited by 1,077 publications

References 8 publications

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Surface Parameter Estimation Using Bistatic Polarimetric X-Band Measurements

Characterization of wet soils in the 2-18 GHz frequency range

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