A model for soil surface roughness influence on the spectral response of bare soils in the visible and near-infrared range

Cierniewski, Jerzy

doi:10.1016/0034-4257(87)90073-3

Cited by 80 publications

(41 citation statements)

References 4 publications

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“…But these differences do not change the shape of their spectra in the wavelength function. The lower level of the spectra of soil samples with higher roughness states is due to shadows created by aggregates and soil particles (Cierniewski 1987 The spectra level of the soil samples in R1, R2 and R3 roughness states also depends on the H s , which has an effect on an observation area. The higher H s , the greater the observed area.…”

Section: Resultsmentioning

confidence: 99%

“…The spectra of soil surface with clods and aggregates are different in comparison with homogenized and smooth soil surface (Cierniewski 1999). Soil aggregates create irregular shapes of soil surface and result in shadowing of a part of the soil surface (Cierniewski 1987). Increase of the soil irregularities result in increase of shaded areas and decrease of the soil reflectance (Matthias et al 2000).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

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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“…Much research has been done on quantifying and removing these effects from the soil spectra [1,19,20,[29][30][31][32][33][34][35][36][37][38][39][40]. However, most research is focused on the effect on specific soil properties (e.g., [34,36]); focus only on specific spectroscopy techniques (e.g., [35]); or are laboratory based (e.g., [33]).…”

Section: Introductionmentioning

confidence: 99%

“…For soil moisture it is known that the reflectance decreases with higher soil moisture, the water absorption features at around 1400 and 1900 nm are more affected than the rest of the spectra [20,36,37,40]. For soil surface roughness it is known that for increasing roughness, the reflectance is generally lower, due to self-shadows [19,32,33]. Eliminating these short-term processes of meteorology and land management is necessary in order to use a multi-temporal composite for deriving soil properties.…”

Section: Introductionmentioning

confidence: 99%

Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

2016

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An increasing demand for full spatio-temporal coverage of soil information drives the growing use of soil spectroscopy. Soil spectroscopy application performed under laboratory conditions or in-field studies in semi-arid areas have shown promising results. However, when acquiring data in temperate zones, limitations by vegetation-free coverage, variation in soil moisture and management are driving coherent spatio-temporal data collection. This study explores the use of multi-temporal imaging spectroscopy data to increase the total mapping area of bare soils in a heterogeneous agricultural landscape. Spectrally and spatially high-resolution data from the Airborne Prism Experiment (APEX) were collected in September 2013, April 2014 and April 2015. Bare soils in all acquisitions were identified. To eliminate short-term differences in soil moisture and soil surface roughness, the empirical line method was used to calibrate the reflectance values of the singular images (2013 and 2015) towards the singular image with most bare soil pixels (2014). Difference indicators show that the calibration was successful (decrease in root mean square difference and angle difference, increase in R 2 and gain and offset close to one and zero). Finally, the multi-temporal composite image contained more than double the amount of bare soil pixels as compared to a singular acquisition. Summary statistics show that reflectance values of the multi-temporal composite approximate the single image data of 2014 (mean and standard deviation of 2014: 24.2 ± 8.9 vs. 24.0 ± 9.5 for the multi-temporal composite of 2013, 2014 and 2015). This indicates that global differences in soil moisture and land management have been corrected for. As a result, an improved spatial representation of soil parameters can be retrieved from the composite data. Spatial distribution of the correction factors and analysis of the spatial variability of all images, however, indicate that non-linear, short-term differences like variation in soil moisture and land management largely influence the result of the multi-temporal composite. Quantification and attribution of those factors will be required in the future to allow correcting for them.

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“…In the Monte Carlo reflectance model of Cooper and Smith (1985) the soil surface is described with a cosine function in one or two directions. The models of Cierniewski (1987) and Irons et ul. (1992) describe soil aggregates with regularly spaced equal-sized spheres.…”

Section: Introductionmentioning

confidence: 99%

Inferring the roughness of desert rocky surfaces from their bidirectional reflectance data

Karnieli

Cierniewski

2001

Advances in Space Research

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Bare soil surfaces show variation in their upward radiance depending on the direction of irradiating solar energy and the direction along which the reflected energy is viewed. Desert surfaces can exhibit both a backscattering and forward scattering characteristics.The authors tried to infer the roughness of desert rocky surfaces from their bidirectional reflectance data sets, using a geometrical reflectance model inversion technique. To achieve this, virtual equivalents of the surfaces were generated. These virtual surfaces simulate real surfaces with equal-sized opaque spheroids regularly dispersed on freely sloping plane. The capabilities of the model inversion were tested on semiarid surfaces of the Negev desert including rough rocks and smooth dune sands. 0 200 I COSPAR. Published by

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A model for soil surface roughness influence on the spectral response of bare soils in the visible and near-infrared range

Cited by 80 publications

References 4 publications

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

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

Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

Inferring the roughness of desert rocky surfaces from their bidirectional reflectance data

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