Analysis of the Influence of Soil Roughness, Surface Crust and Soil Moisture on Spectral Reflectance

Dilawari, Geetika; Kaleita, Amy L.

doi:10.13031/2013.20590

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The wavelengths fell in the 810–820 nm range for HSD, and from 725 to 803 nm for T 3D . Both spectral ranges have previously been found to encompass the largest differences in reflectance associated with changes in SSR following rainfall (Dilawari & Kaleita, 2006). A previous study examining rough soil surfaces from various directions also found wavelengths longer than 700 nm to be best for detecting changes in roughness after artificial rainfall (Croft et al., 2012).…”

Section: Resultsmentioning

confidence: 87%

“…Earlier studies on spectral properties of SSR subjected to rainfall concerned mainly very heavy and crusted soils (Anderson & Kuhn, 2008; Ben‐Dor et al., 2003; Croft et al., 2009, 2012; Kaleita & Dilawari, 2006) or did not include quantitative description of surface roughness (Ballard et al., 2012; Ben‐Dor et al., 2003; Chappel et al., 2005, 2006, 2007). However, the present study investigates the changes of texturaly light soils surface spectra under gradual smoothing caused by simulated rainfall.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Soil Surface Roughness Parameters Under Simulated Rainfall Using Spectral Reflectance in Optical Domain

Herodowicz‐Mleczak,

Piekarczyk,

Ratajkiewicz

et al. 2023

Earth and Space Science

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The purpose of the study was to evaluate the possibility of parameterizing the state of soil surface roughness (SSR) based on proximal measurements of spectral reflectance in the VIS‐NIR range, which is important for the needs of monitoring the state of soil surfaces. SSR should be constantly monitored as it provides an insight into a range of hydrological and erosive soil processes and improves the interpretation of remote sensing data. SSR and the spectral reflectance of three texturally different soils were measured under simulated rainfall in laboratory condition. The relationship between the SSR parameters and soil spectra was determined using regression random forest models. Various spectral data processing methods were tested and the best wavelengths for SSR description after rainfall were found. Two roughness indices were used to describe the SSR: Height Standard Deviation (HSD) and T3D (Tortuosity index). Although both shared a significant correlation with SSR, the T3D index demonstrated a more pronounced rainfall effect and a closer correlation with spectral data than HSD. The best determination of T3D was obtained with the raw spectra (RAW) (R2 = 0.71), as well as with spectra transformed with the baseline alignment first derivative (BA1d) method (R2 = 0.71) or the Savitzky‐Golay (SG) method (R2 = 0.69). Different wavelengths were the best SSR predictors depending on the spectral transformation method (VIPs ‐ Variable Importance in Projection). For both roughness indices, the NIR wavelengths (725–820 nm) yielded the highest VIP Score in models based on RAW spectra, while those in the VIS region (450–772 nm) were most important in models based on transformed spectra.

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Section: Resultsmentioning

confidence: 87%