The Experts below are selected from a list of 10239 Experts worldwide ranked by ideXlab platform
Nikolaus J Kuhn - One of the best experts on this subject based on the ideXlab platform.
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evaluating the influence of Surface Soil moisture and Soil Surface Roughness on optical directional reflectance factors
European Journal of Soil Science, 2014Co-Authors: Holly Croft, Karen Anderson, Nikolaus J KuhnAbstract:Summary Fine-scale information on Soil Surface Roughness (SSR) is needed for calculating heat budgets, monitoring Soil degradation and parameterizing Surface runoff and sediment transfer models. Previous work has demonstrated the potential of using hyperspectral, hemispherical conical reflectance factors (HCRFs) to retrieve the SSR of different Soil crusting states. However, this was achieved by using dry Soil Surfaces, generated in controlled laboratory conditions. The primary aim of this study was therefore to test the impact that in situ variations in Surface Soil moisture (SSM) content had on the ability of directional reflectance factors to characterize SSR conditions. Five Soil plots (20 cm × 20 cm in area) representing different agricultural conditions were subjected to different durations of natural rainfall to produce a range of different levels of SSR. The values of SSM varied from 8.7 to 20.1% across all Soil plots. Point laser data (4-mm sample spacing) were geostatistically analysed to give a spatially-distributed measure of SSR, giving sill variance values from 3.2 to 23.0. The HCRFs from each Soil state were measured using a ground-based hyperspectral spectroradiometer for a range of viewing zenith angles from extreme forward-scatter (θr = −60°) to extreme back-scatter (θr = +60°) at a 10° sampling resolution in the solar principal plane. The results showed that despite a large range of SSM values, forward-scattered reflectance factors exhibited a very strong relationship with SSR (R2 = 0.84 at θr = −60°). Our findings demonstrate the operational potential of HCRFs for providing spatially-distributed SSR measurements, across spatial extents containing spatio-temporal variations in SSM content.
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reflectance anisotropy for measuring Soil Surface Roughness of multiple Soil types
Catena, 2012Co-Authors: Holly Croft, Karen Anderson, Nikolaus J KuhnAbstract:Abstract Information on Soil Surface Roughness at the centimetre scale is needed for inclusion in a range of physical and functional algorithms including heat budgets, runoff and sediment transfer models, and can also be used to understand Soil degradation processes. Previous work has shown that such information can be obtained from multiple view angle measurements of hyperspectral Hemispherical Conical Reflectance Factors (HCRFs), but the issue of whether this technique works on Soils of different biochemical composition has not yet been demonstrated. The objective of this work was therefore to determine the capability of these approaches for discriminating Soil Surface Roughness conditions when different Soil types are considered. Five Soil types with varying biochemical properties were subjected to artificial rainfall, producing a sequence of Soil states of progressively declining Soil Surface Roughness. Point laser data (2 mm sample spacing) were geostatistically analysed to give a spatially-distributed measure of Surface Roughness. HCRFs from the Soil states were measured using a ground-based hyperspectral spectroradiometer for a range of viewing zenith angles in the solar principal plane from the extreme forwardscatter (− 60°) to the extreme backscatter (+ 60°) at 10° sampling resolution in the solar principal plane. A directional index (Anisotropy Measure; AM) was determined, using a ratio between extreme forward-scattered and backscattered HCRFs. Regression analysis of AM against a geostatistically-derived value of Soil Surface Roughness (sill variance) was used to test the ability of the AM for description of Surface Roughness for all Soil types. The results show that use of a directional AM index dramatically improved the relationship with sill variance compared to the use of a single viewing angle (R2 = 0.68 at θr = 40°; R2 = 0.88 (AM)), demonstrating the great potential of this approach for compensating for spectral differences between different Soil types. The results provide an empirical and theoretical basis for the future retrieval of spatially-distributed assessments of Soil Surface structure across larger spatial extents.
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characterizing Soil Surface Roughness using a combined structural and spectral approach
European Journal of Soil Science, 2009Co-Authors: Holly Croft, Karen Anderson, Nikolaus J KuhnAbstract: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 (θr=-60° to +60°) and illumination angle conditions (θi= 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 (R2= 0.94 where θr=-60°, θi= 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.
Jesus Alvarezmozos - One of the best experts on this subject based on the ideXlab platform.
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evaluation of terrestrial laser scanner and structure from motion photogrammetry techniques for quantifying Soil Surface Roughness parameters over agricultural Soils
Earth Surface Processes and Landforms, 2020Co-Authors: Ale Martinezagirre, Jesus Alvarezmozos, Milutin Milenkovic, Norbert Pfeifer, Rafael Gimenez, Jose Manuel Valle, Alvaro RodriguezAbstract:This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Grant BES‐2012‐054521, Project CGL2011‐24336, Project CGL2015‐64284‐C2‐1‐R, and Project CGL2016‐75217‐R (MINECO/FEDER, EU).
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influence of Surface Roughness sample size for c band sar backscatter applications on agricultural Soils
IEEE Geoscience and Remote Sensing Letters, 2017Co-Authors: Ale Martinezagirre, Jesus Alvarezmozos, Hans Lievens, Niko E C Verhoest, Rafael GimenezAbstract:Soil Surface Roughness determines the backscatter coefficient observed by radar sensors. The objective of this letter was to determine the Surface Roughness sample size required in synthetic aperture radar applications and to provide some guidelines on Roughness characterization in agricultural Soils for these applications. With this aim, a data set consisting of ten ENVISAT/ASAR observations acquired coinciding with Soil moisture and Surface Roughness surveys has been processed. The analysis consisted of: 1) assessing the accuracies of Roughness parameters $s$ and ${l}$ depending on the number of 1-m-long profiles measured per field; 2) computing the correlation of field average Roughness parameters with backscatter observations; and 3) evaluating the goodness of fit of three widely used backscatter models, i.e., integral equation model (IEM), geometrical optics model (GOM), and Oh model. The results obtained illustrate a different behavior of the two Roughness parameters. A minimum of 10–15 profiles can be considered sufficient for an accurate determination of $s$ , while 20 profiles might still be not enough for accurately estimating ${l}$ . The correlation analysis revealed a clear sensitivity of backscatter to Surface Roughness. For sample sizes >15 profiles, ${R}$ values were as high as 0.6 for ${s}$ and ~0.35 for ${l}$ , while for smaller sample sizes ${R}$ values dropped significantly. Similar results were obtained when applying the backscatter models, with enhanced model precision for larger sample sizes. However, IEM and GOM results were poorer than those obtained with the Oh model and more affected by lower sample sizes, probably due to larger uncertainly of ${l}$ .
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influence of Surface Roughness measurement scale on radar backscattering in different agricultural Soils
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Ale Martinezagirre, Jesus Alvarezmozos, Hans Lievens, Niko E C VerhoesAbstract:Soil Surface Roughness strongly affects the scattering of microwaves on the Soil Surface and determines the backscattering coefficient ( $\sigma ^{0})$ observed by radar sensors. Previous studies have shown important scale issues that compromise the measurement and parameterization of Roughness especially in agricultural Soils. The objective of this paper was to determine the Roughness scales involved in the backscattering process over agricultural Soils. With this aim, a database of 132 5-m profiles taken on agricultural Soils with different tillage conditions was used. These measurements were acquired coinciding with a series of ENVISAT/ASAR observations. Roughness profiles were processed considering three different scaling issues: 1) influence of measurement range; 2) influence of low-frequency Roughness components; and 3) influence of high-frequency Roughness components. For each of these issues, eight different Roughness parameters were computed and the following aspects were evaluated: 1) Roughness parameters values; 2) correlation with $\sigma ^{0}$ ; and 3) goodness-of-fit of the Oh model. Most parameters had a significant correlation with $\sigma ^{0}$ especially the fractal dimension, the peak frequency, and the initial slope of the autocorrelation function. These parameters had higher correlations than classical parameters such as the standard deviation of Surface heights or the correlation length. Very small differences were observed when longer than 1-m profiles were used as well as when small-scale Roughness components ( 100 cm) were disregarded. In conclusion, the medium-frequency Roughness components (scale of 5–100 cm) seem to be the most influential scales in the radar backscattering process on agricultural Soils.
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assessment of sar retrieved Soil moisture uncertainty induced by uncertainty on modeled Soil Surface Roughness
International Journal of Applied Earth Observation and Geoinformation, 2012Co-Authors: E De Keyser, Jesus Alvarezmozos, Hans Lievens, Hilde Vernieuwe, B De Baets, Niko E C VerhoestAbstract:Abstract The Integral Equation Model (IEM) is frequently used to retrieve moisture content of bare Soils from synthetic aperture radar (SAR) images. This physically-based backscatter model requires Surface Roughness parameters, generally obtained by in situ measurements, which unfortunately often result in inaccurately retrieved Soil moisture contents. Furthermore, when the retrieved Soil moisture contents need to be used in data assimilation applications, it is important to also assess the retrieval uncertainty. Therefore, in this paper a regression-based method is developed that allows for the parameterization of Roughness and that provides an estimation of its uncertainty by means of a probability distribution. By further propagating this distribution through the inversion of the IEM, a probability distribution of Soil moisture content is obtained. It was found that 70% of the thus obtained distributions are skewed and non-normal. Furthermore, it is shown that their interquartile range varies depending on Soil moisture conditions. Comparison of Soil moisture measurements with the retrieved median values of the Soil moisture histograms results in a root mean square error (RMSE) of approximately 3.5 vol%.
Ale Martinezagirre - One of the best experts on this subject based on the ideXlab platform.
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evaluation of terrestrial laser scanner and structure from motion photogrammetry techniques for quantifying Soil Surface Roughness parameters over agricultural Soils
Earth Surface Processes and Landforms, 2020Co-Authors: Ale Martinezagirre, Jesus Alvarezmozos, Milutin Milenkovic, Norbert Pfeifer, Rafael Gimenez, Jose Manuel Valle, Alvaro RodriguezAbstract:This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Grant BES‐2012‐054521, Project CGL2011‐24336, Project CGL2015‐64284‐C2‐1‐R, and Project CGL2016‐75217‐R (MINECO/FEDER, EU).
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influence of Surface Roughness sample size for c band sar backscatter applications on agricultural Soils
IEEE Geoscience and Remote Sensing Letters, 2017Co-Authors: Ale Martinezagirre, Jesus Alvarezmozos, Hans Lievens, Niko E C Verhoest, Rafael GimenezAbstract:Soil Surface Roughness determines the backscatter coefficient observed by radar sensors. The objective of this letter was to determine the Surface Roughness sample size required in synthetic aperture radar applications and to provide some guidelines on Roughness characterization in agricultural Soils for these applications. With this aim, a data set consisting of ten ENVISAT/ASAR observations acquired coinciding with Soil moisture and Surface Roughness surveys has been processed. The analysis consisted of: 1) assessing the accuracies of Roughness parameters $s$ and ${l}$ depending on the number of 1-m-long profiles measured per field; 2) computing the correlation of field average Roughness parameters with backscatter observations; and 3) evaluating the goodness of fit of three widely used backscatter models, i.e., integral equation model (IEM), geometrical optics model (GOM), and Oh model. The results obtained illustrate a different behavior of the two Roughness parameters. A minimum of 10–15 profiles can be considered sufficient for an accurate determination of $s$ , while 20 profiles might still be not enough for accurately estimating ${l}$ . The correlation analysis revealed a clear sensitivity of backscatter to Surface Roughness. For sample sizes >15 profiles, ${R}$ values were as high as 0.6 for ${s}$ and ~0.35 for ${l}$ , while for smaller sample sizes ${R}$ values dropped significantly. Similar results were obtained when applying the backscatter models, with enhanced model precision for larger sample sizes. However, IEM and GOM results were poorer than those obtained with the Oh model and more affected by lower sample sizes, probably due to larger uncertainly of ${l}$ .
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influence of Surface Roughness measurement scale on radar backscattering in different agricultural Soils
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Ale Martinezagirre, Jesus Alvarezmozos, Hans Lievens, Niko E C VerhoesAbstract:Soil Surface Roughness strongly affects the scattering of microwaves on the Soil Surface and determines the backscattering coefficient ( $\sigma ^{0})$ observed by radar sensors. Previous studies have shown important scale issues that compromise the measurement and parameterization of Roughness especially in agricultural Soils. The objective of this paper was to determine the Roughness scales involved in the backscattering process over agricultural Soils. With this aim, a database of 132 5-m profiles taken on agricultural Soils with different tillage conditions was used. These measurements were acquired coinciding with a series of ENVISAT/ASAR observations. Roughness profiles were processed considering three different scaling issues: 1) influence of measurement range; 2) influence of low-frequency Roughness components; and 3) influence of high-frequency Roughness components. For each of these issues, eight different Roughness parameters were computed and the following aspects were evaluated: 1) Roughness parameters values; 2) correlation with $\sigma ^{0}$ ; and 3) goodness-of-fit of the Oh model. Most parameters had a significant correlation with $\sigma ^{0}$ especially the fractal dimension, the peak frequency, and the initial slope of the autocorrelation function. These parameters had higher correlations than classical parameters such as the standard deviation of Surface heights or the correlation length. Very small differences were observed when longer than 1-m profiles were used as well as when small-scale Roughness components ( 100 cm) were disregarded. In conclusion, the medium-frequency Roughness components (scale of 5–100 cm) seem to be the most influential scales in the radar backscattering process on agricultural Soils.
Holly Croft - One of the best experts on this subject based on the ideXlab platform.
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evaluating the influence of Surface Soil moisture and Soil Surface Roughness on optical directional reflectance factors
European Journal of Soil Science, 2014Co-Authors: Holly Croft, Karen Anderson, Nikolaus J KuhnAbstract:Summary Fine-scale information on Soil Surface Roughness (SSR) is needed for calculating heat budgets, monitoring Soil degradation and parameterizing Surface runoff and sediment transfer models. Previous work has demonstrated the potential of using hyperspectral, hemispherical conical reflectance factors (HCRFs) to retrieve the SSR of different Soil crusting states. However, this was achieved by using dry Soil Surfaces, generated in controlled laboratory conditions. The primary aim of this study was therefore to test the impact that in situ variations in Surface Soil moisture (SSM) content had on the ability of directional reflectance factors to characterize SSR conditions. Five Soil plots (20 cm × 20 cm in area) representing different agricultural conditions were subjected to different durations of natural rainfall to produce a range of different levels of SSR. The values of SSM varied from 8.7 to 20.1% across all Soil plots. Point laser data (4-mm sample spacing) were geostatistically analysed to give a spatially-distributed measure of SSR, giving sill variance values from 3.2 to 23.0. The HCRFs from each Soil state were measured using a ground-based hyperspectral spectroradiometer for a range of viewing zenith angles from extreme forward-scatter (θr = −60°) to extreme back-scatter (θr = +60°) at a 10° sampling resolution in the solar principal plane. The results showed that despite a large range of SSM values, forward-scattered reflectance factors exhibited a very strong relationship with SSR (R2 = 0.84 at θr = −60°). Our findings demonstrate the operational potential of HCRFs for providing spatially-distributed SSR measurements, across spatial extents containing spatio-temporal variations in SSM content.
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reflectance anisotropy for measuring Soil Surface Roughness of multiple Soil types
Catena, 2012Co-Authors: Holly Croft, Karen Anderson, Nikolaus J KuhnAbstract:Abstract Information on Soil Surface Roughness at the centimetre scale is needed for inclusion in a range of physical and functional algorithms including heat budgets, runoff and sediment transfer models, and can also be used to understand Soil degradation processes. Previous work has shown that such information can be obtained from multiple view angle measurements of hyperspectral Hemispherical Conical Reflectance Factors (HCRFs), but the issue of whether this technique works on Soils of different biochemical composition has not yet been demonstrated. The objective of this work was therefore to determine the capability of these approaches for discriminating Soil Surface Roughness conditions when different Soil types are considered. Five Soil types with varying biochemical properties were subjected to artificial rainfall, producing a sequence of Soil states of progressively declining Soil Surface Roughness. Point laser data (2 mm sample spacing) were geostatistically analysed to give a spatially-distributed measure of Surface Roughness. HCRFs from the Soil states were measured using a ground-based hyperspectral spectroradiometer for a range of viewing zenith angles in the solar principal plane from the extreme forwardscatter (− 60°) to the extreme backscatter (+ 60°) at 10° sampling resolution in the solar principal plane. A directional index (Anisotropy Measure; AM) was determined, using a ratio between extreme forward-scattered and backscattered HCRFs. Regression analysis of AM against a geostatistically-derived value of Soil Surface Roughness (sill variance) was used to test the ability of the AM for description of Surface Roughness for all Soil types. The results show that use of a directional AM index dramatically improved the relationship with sill variance compared to the use of a single viewing angle (R2 = 0.68 at θr = 40°; R2 = 0.88 (AM)), demonstrating the great potential of this approach for compensating for spectral differences between different Soil types. The results provide an empirical and theoretical basis for the future retrieval of spatially-distributed assessments of Soil Surface structure across larger spatial extents.
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characterizing Soil Surface Roughness using a combined structural and spectral approach
European Journal of Soil Science, 2009Co-Authors: Holly Croft, Karen Anderson, Nikolaus J KuhnAbstract: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 (θr=-60° to +60°) and illumination angle conditions (θi= 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 (R2= 0.94 where θr=-60°, θi= 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.
Shadrack O Nyawade - One of the best experts on this subject based on the ideXlab platform.
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effect of potato hilling on Soil temperature Soil moisture distribution and sediment yield on a sloping terrain
Soil & Tillage Research, 2018Co-Authors: Shadrack O Nyawade, N K Karanja, Charles K K Gachene, Elmar Schultegeldermann, M ParkerAbstract:Abstract Soil erosion rates are exacerbated in sloping arable lands of Central Kenya due mainly to the high Soil disturbance caused by potato hilling. A field study was conducted in runoff plots to quantify the effect of potato hilling on Soil loss, Soil moisture distribution and Soil temperature. Three hilling practices; hilling performed at before crop emergence (pre-hilling), one-pass hilling (at 15 days after potato emergence), the conventional two-pass hilling (at 15 and 30 days after potato emergence), and the control (non-hilling) constituted the treatments. Root length density, vegetal cover, Soil Surface Roughness and Soil water infiltration capacity were quantified at different stages of potato growth and related with the sediment yield. Soil temperature and Soil moisture contents were monitored using Onset HOBO sensor probes throughout the potato growth cycle. Compared to the conventional two-pass hilling, pre-hilling increased the Soil moisture content by 6% and lowered the Soil temperature by up to 3.4 °C at crop emergence, thus optimized tuber germination and growth. This ensured earlier canopy closure and reduced the cumulative sediment yield by 12 t/ha. The increased Surface Roughness resulting from pre-hilled ridges puddled the Surface water and increased the Soil water infiltration rate by 7 to 9 mm/hr compared to the non-hilled plots. Planting potatoes in pre-hilled plots has a potential to optimize the Soil temperature and Soil moisture conditions and can reduce the high Soil erosion rates in sloping arable lands.