Assessment of soil surface roughness statistics for microwave remote sensing applications using a simple photogrammetric acquisition system

Marzahn, Philip; Rieke‐Zapp, Dirk; Ludwig, Ralf

doi:10.1016/j.isprsjprs.2012.06.005

Cited by 49 publications

(49 citation statements)

References 34 publications

(57 reference statements)

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“…For the 22 m 2 sample plot R30 we analyzed the dependency of the roughness values in context of the sample size according to [7] for the original and the detrended surface. While for the detrended surface the measurements can be assumed to be representative at a sample size of 1.5 m 2 , the original surface of R30 shows a strong dependency of the roughness values from the sample size which reflects the topographic impact.…”

Section: Resultsmentioning

confidence: 99%

“…On the ground truth site, Marzahn et al [7] showed a significant difference in roughness values originating from the orientation of roughness measurements to the seedbed rows. Mattia [8] developed a backscatter model based on the findings of [6] which is able to reproduce the very narrow directional scatter pattern observed in [4] of only a few tenths of a degree.…”

Section: Introductionmentioning

confidence: 99%

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Decomposing Dual Scale Soil Surface Roughness for Microwave Remote Sensing Applications

2012

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Soil surface roughness, as investigated in this study, is decomposed in a dual scale process. Therefore, we investigated photogrammetrically acquired roughness information over different agricultural fields in the size of 6-22 m 2 and decomposed them into a dual scale process by using geostatistical techniques. For the characterization of soil surface roughness, we calculated two different roughness indices (the RMS height s and the autocorrelation length l) differing significantly for each scale. While we could relate the small scale roughness pattern clearly to the seedbed rows, the larger second scale pattern could be related to the appearance of wheel tracks of the tillage machine used. As a result, major progress was made in the understanding of the different scales in soil surface roughness characterization and its quantification possibilities.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decomposing Dual Scale Soil Surface Roughness for Microwave Remote Sensing Applications

2012

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“…These measurements allowed the creation of a digital elevation model (DEM), and calculation of height standard deviation (HSD) parameter of tested surfaces by using TNTMips software. This parameter describes a shape of soil surface used within its delineated basic DEM unit (Marzahn et al 2012). The calibration procedure was performed, before each measurement, with a special white panel to determine the centre of the layout.…”

Section: Methodsmentioning

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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“…The rmsh from the UAV photos ranged from 0.024 to 0.381 cm. This falls in the random roughness class [36] indicative of soil aggregates and clods. Except for very few areas, the roughness was consistent in most of the study area.…”

Section: Oh Soil Moisture Retrievalmentioning

confidence: 99%

“…The Auto Correlation Function (ACF) describes the degree of correlation between the height Z i at location i and the height Z i+j at a horizontal distance x from i [33]- [36].…”

Section: Surface Roughness Datamentioning

confidence: 99%

Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

Kirimi¹,

Kuria²,

Thonfeld³

et al. 2016

ARS

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Assessment of soil surface roughness statistics for microwave remote sensing applications using a simple photogrammetric acquisition system

Cited by 49 publications

References 34 publications

Decomposing Dual Scale Soil Surface Roughness for Microwave Remote Sensing Applications

Decomposing Dual Scale Soil Surface Roughness for Microwave Remote Sensing Applications

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

Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

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