Reflectance Properties of Soils

Baumgardner, M. F.; Silva, LeRoy F.; Biehl, L. L.; Stoner, E. R.

doi:10.1016/s0065-2113(08)60672-0

Cited by 401 publications

(270 citation statements)

References 43 publications

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“…Simple and extended summaries of the relationship between laws of radiation, absorption and emissivity, the physics of measuring extended sources in the field and the relationship of Bidirectional Reflectance Distribution Function (BRDF) to reflectance measurements can be found in Nicodemus et al (1977), Horn and Sjoberg (1978), Silva (1978), Robinson and Biehl (1979), Duggin and Philipson (1982), Baumgardner et al (1985), Milton (1987), Deering (1989), Pinter et al (1990), Hapke (1993), Milton et al (1995), Jupp (1997), Schaepman (1998) and Rees (2001). Many of the standardised measurements are based on the bidirectional reflectance factor (BRF), summarised in Pfitzner et al (2005).…”

Section: Field Design and Metadata Recording Standard Issuesmentioning

confidence: 99%

“…Experience has shown the stabilising pole is required to reduce the variations in spectral measurements seen whenever wind is a limiting factor. Measurements are made at a sensor zenith angle of 0° (nadir) and an 8° FOV, so that angle of acceptance is less than 20° full angle (Baumgardner et al, 1985;Deering, 1989).…”

Section: Viewing Geometry In the Fieldmentioning

confidence: 99%

“…Numerous references discuss the relationship between physical/biophysical variables and EMR, including: the way in which minerals absorb photons and their associated wavelength dependency (for example, see Hunt and Salisbury, 1970;Hunt et al, 1971a and b;Rowan et al, 1977;Hunt and Ashley, 1979;Hunt, 1977 and1979;Goetz and Rowan, 1981); the spectral reflectance of vegetation (Collins, 1978;Horler et al, 1983;Milton and Mouat, 1989;Boochs et al, 1990;Elvidge, 1990;King et al, 1995;Campbell, 1996;Dawson and Curran, 1998;Datt, 1999 and2000); and, characteristics of soils (Baumgardner et al, 1985;Irons et al, 1989).…”

Section: Target and Wavelength Dependencymentioning

confidence: 99%

See 2 more Smart Citations

A standard design for collecting vegetation reference spectra: Implementation and implications for data sharing

Pfitzner¹,

Bollhöfer²,

Carr³

2006

Journal of Spatial Science

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Section: Field Design and Metadata Recording Standard Issuesmentioning

confidence: 99%

Section: Viewing Geometry In the Fieldmentioning

confidence: 99%

Section: Target and Wavelength Dependencymentioning

confidence: 99%

See 1 more Smart Citation

A standard design for collecting vegetation reference spectra: Implementation and implications for data sharing

Pfitzner¹,

Bollhöfer²,

Carr³

2006

Journal of Spatial Science

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“…There are far fewer studies that have applied this approach to the relationship between surfaces degraded by water erosion and changes in surface spectral reflectance (e.g. Latz et al, 1984;Baumgardner et al, 1985). Most laboratory and field experiments on soil spectral reflectance have naturally focused on bare soil because of the ease with which change at the soil surface may be detected.…”

Section: Spectral Reflectance Measurementmentioning

confidence: 99%

Using on‐nadir spectral reflectance to detect soil surface changes induced by simulated rainfall and wind tunnel abrasion

Chappell

Zobeck

Brunner

2005

Earth Surf Processes Landf

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The surface susceptibility to erosion (erodibility) is an important component of soil erosion models. Many studies of wind erosion have shown that even relatively small changes in surface conditions can have a considerable effect on the temporal and spatial variability of dust emissions. One of the main difficulties in measuring erodibility is that it is controlled by a number of highly variable soil factors. Collection of these data is often limited in scale because in situ measurements are labour-intensive and very time-consuming. To improve wind erosion model predictions over several spatial and temporal scales simultaneously, there is a requirement for a non-invasive approach that can be used to rapidly assess changes in the compositional and structural nature of a soil surface in time and space. Spectral reflectance of the soil surface appears to meet these desirable requirements and it is controlled by properties that affect the soil erodibility.Three soil surfaces were modified using rainfall simulation and wind tunnel abrasion experiments. Observations of those changes were made and recorded using digital images and on-nadir spectral reflectance. The results showed clear evidence of the information content in the spectral domain that was otherwise difficult to interpret given the complicated interrelationships between soil composition and structure. Changes detected at the soil surface included the presence of a crust produced by rainsplash, the production of loose erodible material covering a rain crust and the selective erosion of the soil surface. The effect of rainsplash and aeolian abrasion was different for each soil tested and crust abrasion was shown to decrease as rainfall intensity increased. The relative contributions of the eroded material from each soil surface to trapped mixtures of material assisted the erodibility assessment. Ordination analyses within each of two important soil types explained significant amounts of the variation in the reflectance of all wavebands by treatments of the soil and hence changes in the soil surface. The results show that soil surface conditions within a soil type are an underestimated source of variation in the characterization of soil surface erodibility and in the remote sensing of soil.

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“…Increase of the soil irregularities result in increase of shaded areas and decrease of the soil reflectance (Matthias et al 2000). Furthermore, fine-grains have more rounded shapes and greater reflectance spectra, while more cavities and gaps appear in coarse grains, which are traps for incident radiation (Baumgardner et al 1985;Mikhajlova, Orlov 1986;Cierniewski, Kuśnierek 2010).…”

Section: Introductionmentioning

confidence: 99%

Influence of the Distance Between a Reflectance Sensor and Soil Samples with Different Roughness on Their Spectra

Herodowicz

2017

pjss

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The study assessed the influence of the distance between a reflectance sensor and soil samples with various roughness states (R1-the lowest, R2 -the medium, R3 -the highest roughness state) on their spectra level, under laboratory condition. Studied soil samples were illuminated at three light source zenith angles (θ s equal to 20°, 40°, 60°) and observed by the sensor to the nadir, from various distances (H s ) from 10 to 54 cm. These dark (the Mollic Gleyic Fluvisol) and light (the Cutanic Stagnic Luvisol) soil materials with their minimum roughness were characterized by diffused reflectance spectra. The relative differences (RD) between the spectra level of soil samples with R1, R2, R3 roughness states and the diffused reflectance level of soil materials were calculated with 1 nm interval in range of 420-2,300 nm. Higher roughness state and higher θ s , result in higher RD. Thus, for the dark and light soil samples with R3 roughness state and illuminated at θ s = 60°,the RD are the highest reached 63 and 39% (H s =54 cm) and reached 77 and 63% (H s =10 cm), respectively. The spectra level of the soil samples in R1 and R3 roughness states, illuminated at θ s =20° and soil samples with R1 roughness, illuminated at θ s =60°, reached a stable level, at a specific H s . It means, that a spectra does not significantly change with a further increase H s . However, the soil samples in R3 roughness, illuminated at θ s =60° have not reached the stability.

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Reflectance Properties of Soils

Cited by 401 publications

References 43 publications

A standard design for collecting vegetation reference spectra: Implementation and implications for data sharing

A standard design for collecting vegetation reference spectra: Implementation and implications for data sharing

Using on‐nadir spectral reflectance to detect soil surface changes induced by simulated rainfall and wind tunnel abrasion

Influence of the Distance Between a Reflectance Sensor and Soil Samples with Different Roughness on Their Spectra

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