Sensitivity of radar backscatter to desert surface roughness

Charlton, Matthew; White, Kevin

doi:10.1080/01431160500491740

Cited by 9 publications

(7 citation statements)

References 26 publications

(35 reference statements)

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“…However, traditional data collection methods cannot easily provide spatially‐ and temporally‐distributed data over large areas for assimilation into such models (Jetten et al , 1999; Le Bissonnais et al , 2005; Baghdadi et al , 2008), whereas remote sensing methods can (King et al , 2005). Most previous remote sensing studies have focussed on the use of microwave backscatter (Ridley et al , 1996; Baghdadi et al , 2002; Charlton & White, 2006), but because of the long wavelengths used, these data usually only characterize large roughness variations (i.e. > 1 cm), which are not useful in a fine‐scale context (Baghdadi et al , 2008).…”

Section: Introductionmentioning

confidence: 99%

Characterizing soil surface roughness using a combined structural and spectral approach

Croft

Anderson

Kuhn

2009

European J Soil Science

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The ability to quantitatively and spatially assess soil surface roughness is important in geomorphology and land degradation studies. This paper describes the results of an experiment designed to investigate whether hyperspectral directional reflectance factors can describe fine-scale variations in soil surface roughness. A Canadian silt loam soil was sieved to an aggregate size range of 1-4.75 mm and exposed to five different artificial rainfall durations to produce soils displaying progressively decreasing levels of surface roughness. Each soil state was measured using a point laser profiling instrument at 2 mm spatial resolution, in order to provide information on the structure and spatial arrangement of soil particles. Hyperspectral directional reflectance factors were measured using an Analytical Spectral Devices FieldSpec Pro Spectroradiometer (range 350-2500 nm), at a range of measurement angles (h r ¼ À60°to þ60°) and illumination angle conditions (h i ¼ 28°-74°). Directional reflectance factors varied with illumination and view angles, and with soil structure. Geostatistically-derived indicators of soil surface roughness (sill variance) were regressed with directional reflectance factors. The results showed a strong relationship between directional reflectance and surface roughness (R 2 ¼ 0.94 where h r ¼ À60°, h i ¼ 67°-74°). This fine-scale quasi-natural experiment allowed the control of slope, initial aggregate size and rainfall exposure, permitting an investigation into factors affecting a soil's bidirectional reflectance response. This has highlighted the relationship between fine-scale variations in surface roughness, illumination angle and reflectance response. The results show how the technique could provide a quantitative measure of surface roughness at fine spatial scales.

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Section: Introductionmentioning

confidence: 99%

Characterizing soil surface roughness using a combined structural and spectral approach

Croft

Anderson

Kuhn

2009

European J Soil Science

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“…Although the LAI time series could not effectively detect phenology in this arid area, the backscatter series showed an apparent cycle of temporal variation. Backscatter from desert regions has been reported to depend on the development of vegetation, soil moisture content, and surface roughness (Ulaby, Baltlivala, and Dobson 1978;Ulaby, Moore, and Fung 1982;Kennett and Li 1989;Tansey and Millington 2001;Charlton and White 2006). The phenological pattern observed in the Taklamakan Desert was distributed along the Tarim River and its tributaries (Figure 9).…”

Section: Regional Phenology Detection Resultsmentioning

confidence: 89%

Assessment of the SeaWinds scatterometer for vegetation phenology monitoring across China

Guo

Wang

et al. 2013

International Journal of Remote Sensing

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Vegetation phenology tracks plants' lifecycle events, revealing the response of vegetation to global climate changes. Changes in vegetation phenology also influence fluxes of carbon, water, and energy at local and global scales. In this study, we analysed a time series of Ku-band radar backscatter measurements from the SeaWinds scatterometer on board the Quick Scatterometer (QuickSCAT) to examine canopy phenology from 2003 to 2005 across China. The thaw season SeaWinds backscatter and Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) time series were significantly correlated in 20 of the 22 sites (p < 0.05). A weighted curve-fitting method was applied to detect the start of season and end of season from both data sets. The SeaWinds scatterometer generally detected earlier timing of spring leaf-out and later fall senescence than the MODIS LAI data sets. The SeaWinds backscatter detected phenological metrics in 75.85% of mainland China. Similar spatial patterns were observed from the SeaWinds backscatter and MODIS LAI time series; however, the average standard deviation of the scatterometer-detected metrics was lower than that of MODIS LAI products. Overall, the phenological information from the SeaWinds scatterometer could provide an alternative view on the growth dynamics of land-surface vegetation.

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“…When flooded, the radar backscatter is subjected to wind-generated roughness, increasing its backscattered power σ 0 . When standing water left, low backscattered power can be partially explained by the presence of wind-blown sands that tend to fill between uneven spaces, thus causing the surface to smoothen [30].…”

Section: Episodic Eventsmentioning

confidence: 99%

Seasonality Analysis of Sentinel-1 and ALOS-2/PALSAR-2 Backscattered Power over Salar de Aguas Calientes Sur, Chile

Delsouc

Barber

Gallaud³

et al. 2020

Remote Sensing

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Seasonal changes control the development of salt crust over the Salar de Aguas Calientes Sur located in Andes Highlands, Chile. Precipitations throughout the Altiplanic winter (December to March) and austral winter (June to September) caused ponds to enlarge and surface salt crusts to dissolve driving roughness and dielectric features of the salar surface change over time. A four-year time series backscattering coefficient analysis, obtained by Sentinel 1 and ALOS-2/PALSAR-2 with 10 m of spatial resolution, demonstrated the capability of microwaves to discriminate seasonal patterns illustrated in this paper. Both sensors showed to be sensitive to changes in the surface crust due to weather conditions. Backscattered power gradually increased during the driest months as the rough salt crusts develop and decreased rapidly due to precipitations or flooding events, which lead to a smoothing appearance to radar. The high temporal frequency of acquisition in Sentinel 1 (5–13 scenes/month) allowed the discrimination among climate and annual seasonality and episodic events in the C-band backscatter coefficient. On the other hand, ALOS-2/PALSAR-2 showed subsurface changes at L-band since the salinity of the brine in the soil reduces the penetration depth of backscattered power for shorter wavelengths. Results might be useful to monitor salars with geographic and weather conditions similar to Salar de Aguas Calientes Sur.

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Sensitivity of radar backscatter to desert surface roughness

Cited by 9 publications

References 26 publications

Characterizing soil surface roughness using a combined structural and spectral approach

Characterizing soil surface roughness using a combined structural and spectral approach

Assessment of the SeaWinds scatterometer for vegetation phenology monitoring across China

Seasonality Analysis of Sentinel-1 and ALOS-2/PALSAR-2 Backscattered Power over Salar de Aguas Calientes Sur, Chile

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