Remote sensing applications to hydrology: soil moisture

Jackson, Thomas J.; Schmugge, J.; Engman, Edwin T.

doi:10.1080/02626669609491523

Cited by 209 publications

(99 citation statements)

References 22 publications

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“…Using a simple radiative transfer model that assumes that the Fresnel equation assumptions on profile homogeneity apply [16], a relationship between and soil moisture was predicted for the soil under investigation and is shown in Fig. 3.…”

Section: A Beltsville 1994mentioning

confidence: 99%

Soil water infiltration observation with microwave radiometers

Jackson¹,

Schmugge

O'Neill³

et al. 1998

IEEE Trans. Geosci. Remote Sensing

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Abstract-Experiments were conducted using truck-based microwave radiometers operating at 1.41-(L-band) and 2.65-GHz (S-band) horizontal polarization to observe small plots during and following sprinkler irrigation. These experiments were conducted on a sandy loam soil in 1994 and a silt loam in 1995. Sandy loam soils typically have higher infiltration capabilities than clays, and in our studies, we were not able to exceed this with the irri-

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Section: A Beltsville 1994mentioning

confidence: 99%

Soil water infiltration observation with microwave radiometers

Jackson¹,

Schmugge

O'Neill³

et al. 1998

IEEE Trans. Geosci. Remote Sensing

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“…Although the soil is usually covered with vegetation and the soil surface is not visible, some soil properties can be directly assessed by their spectral signatures (Rossiter, 2005). Some of these notable soil properties are moisture (Jackson et al, 1996), physical and chemical characteristics (Odeh and McBratney, 2000), and salts (Metternicht and Zinck, 2003). Moreover, integrated use of geostatistical techniques with remote sensing data for spatialization is important (Stein et al, 1998).…”

Section: Resumo: Múltiplas Ferramentas Tecnológicas No Mapeamento Digmentioning

confidence: 99%

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Alves

Demattê

Barros

2015

Rev. Bras. Ciênc. Solo

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in recent years, geotechnologies as remote and proximal sensing and attributes derived from digital terrain elevation models indicated to be very useful for the description of soil variability. however, these information sources are rarely used together. therefore, a methodology for assessing and specialize soil classes using the information obtained from remote/proximal sensing, Gis and technical knowledge has been applied and evaluated. two areas of study, in the state of são Paulo, Brazil, totaling approximately 28.000 ha were used for this work. first, in an area (area 1), conventional pedological mapping was done and from the soil classes found patterns were obtained with the following information: a) spectral information (forms of features and absorption intensity of spectral curves with 350 wavelengths -2,500 nm) of soil samples collected at specific points in the area (according to each soil type); b) obtaining equations for determining chemical and physical properties of the soil from the relationship between the results obtained in the laboratory by the conventional method, the levels of chemical and physical attributes with the spectral data; c) supervised classification of Landsat TM 5 images, in order to detect changes in the size of the soil particles (soil texture); d) relationship between classes relief soils and attributes. subsequently, the obtained patterns were applied in area 2 obtain pedological classification of soils, but in GIS (ArcGIS). finally, we developed a conventional pedological mapping in area 2 to which was compared with a digital map, ie the one obtained only with pre certain standards. the proposed methodology had a 79 % accuracy in the first categorical level of Soil Classification System, 60 % accuracy in the second category level and became less useful in the categorical level 3 (37 % accuracy).

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“…Ten roughness profiles, 5 parallel and 5 perpendicular to the tillage row direction, were established in all reference fields during three different ground campaigns. Two main surface roughness parameters, the root mean square surface height (Hrms) and the correlation length (l), were determined from the mean correlation function, which was computed from the digitized soil profiles [3,10]. Significant variations in the values of Hrms were observed between successive ground campaigns, ranging from 0.24 cm (very smooth soils) to 3.4 cm (ploughed soils), resulting mainly from the influence of rain and cultural practices (tillage, sowing…).…”

Section: Soil Roughnessmentioning

confidence: 99%

“…The parameters characterizing agricultural soils have very high spatial and temporal variabilities, and conventional spot soil moisture and surface roughness measurements do not provide an adequate description of this variability. Imaging Synthetic Aperture Radar (SAR) sensors have demonstrated their potential to effectively measure and monitor soil surface characteristics at a high spatial resolution [3][4][5][6][7][8][9]. Over bare agricultural areas, the backscattered radar signal is very sensitive to dielectric (soil moisture) and geometric (roughness) soil surface properties [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters

Gorrab

Zribi

Baghdadi³

et al. 2015

Remote Sensing

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Abstract:The aim of this paper is to analyze the potential of X-band SAR measurements (COSMO-SkyMed and TerraSAR-X) made over bare soils for the estimation of soil moisture and surface geometry parameters at a semi-arid site in Tunisia (North Africa). Radar signals acquired with different configurations (HH and VV polarizations, incidence angles of 26° and 36°) are statistically compared with ground measurements (soil moisture and roughness parameters). The radar measurements are found to be highly sensitive to the various soil parameters of interest. A linear relationship is determined for the radar signals as a function of volumetric soil moisture, and a logarithmic correlation is observed between the radar signals and three surface roughness parameters: the root mean square height (Hrms), the parameter Zs = Hrms 2 /l (where l is the correlation length) and the parameter Zg = Hrms × (Hrms/l) α (where α is the power of the surface height correlation function). The highest dynamic sensitivity is observed for Zg at high incidence angles. Finally, the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models) is compared with SAR measurements. The results provide an indication of the limits of validity of the IEM and Dubois models, for various radar configurations and roughness conditions. Considerable improvements in OPEN ACCESSRemote Sens. 2015, 7 748 the IEM model performance are observed using the Baghdadi-calibrated version of this model.

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Remote sensing applications to hydrology: soil moisture

Cited by 209 publications

References 22 publications

Soil water infiltration observation with microwave radiometers

Soil water infiltration observation with microwave radiometers

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters

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