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Donald F. Post - One of the best experts on this subject based on the ideXlab platform.
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Spectral reflectance and Soil morphology characteristics of Santa Rita Experimental Range Soils
2020Co-Authors: A. Karim Batchily, Donald F. Post, R. Bryant, Donald J. BreckenfeldAbstract:The Santa Rita Experimental Range (SRER) Soils are mostly transported alluvial sediments that occur on the piedmont slope flanking the Santa Rita Mountains in Arizona. The major geomorphic land forms are alluvial fans or fan terraces, but there are also areas of residual Soils formed on granite and limestone bedrock, basin floor, stream terraces, and flood plains. The Soils range in age from recent depositions to Soil material one to two million years of age. We sampled A and B horizons of Soil series from different geomorphic surfaces, and measured the dry spectral reflectance (0.4 to 2.5 μm wavelength) on the sieved less than 2-mm-size fraction. Soil Color (measured with a Chroma Meter), texture, organic carbon, calcium carbonate content, and effervescence properties were determined and correlated to spectral reflectance in selected wavelengths. The Munsell Soil Color value component was most positively correlated to reflec- tance. Soil effervescence and calcium carbonate content, percent sand and clay, and the Munsell Soil Color hue component and redness rating were also significantly correlated to Soil reflectance. Energy reflected from Soil surfaces represents the interaction between many Soil properties, and Soil Color is an integrated expression of many Soil properties. It is the best Soil morphology property to measure to predict the spectral reflectance of Soils, particularly in the visible and near infrared parts of the electromagnetic spectrum. Acknowledgments: The Soils Map GIS data for the Santa Rita Experimental Range (SRER) were completed by Debbie Angell and Dr. M. McClaran, and the manipulation of this data to prepare the geomorphic land form map was prepared by Dawn Browning and Mamadou Niane, University of Arizona, School of Renewable Natural Resources (SRNR). The SRNR Advanced Resource Technology (ART) Laboratory facilities were utilized to create the geomorphic land form map of the SRER. Special thanks are extended to Mary Kay Amistadi and Dr. Jon Chorover, Department of Soil, Water and Environmental Science, University of Arizona, for determining the organic carbon and calcium carbonate content of the SRER Soils. We also thank Dr. M. S. Moran, the USDA Agricultural Research Service, Tucson, AZ, for allowing us to use her Analytical Spectral Device to measure spectral reflectance.
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predicting Soil albedo from Soil Color and spectral reflectance data
Soil Science Society of America Journal, 2000Co-Authors: Donald F. Post, A. Fimbres, A.d. Matthias, E.e. Sano, L. Accioly, A.k. Batchily, L G FerreiraAbstract:The albedo of earth surface features, such as Soil, is an important component of models that define land-surface meteorological processes. If land surfaces have no vegetative cover, Soil properties determine the amount of solar radiation absorbed or reflected. We evaluated the influence of two Soil properties, Soil Color and Soil moisture, on Soil albedo. Two Soil moisture conditions were studied, air dry and wet, defined as the condition when the water films are absorbed by the Soil and no water glistens on the Soil surface. The albedos for 26 U.S. Soils were measured with an Eppley pyranometer, which integrates radiant energy in wavelengths between 0.3 to 2.8 μm. Soil Colors were measured with a Minolta Chroma Meter and spectral reflectance curves from 0.45 to 0.9 μm (measured in 0.1-μm increments) were determined with a multispectral radiometer. All measurements were made on <2-mm smooth Soil surfaces, and the dry and wet data were combined for statistical analyses. Soil albedos were significantly correlated with Munsell Soil Color value (r 2 = 0.93), blue (r 2 = 0.82), green (r 2 = 0.90), red (r 2 = 0.93), near infrared (NIR), (r 2 = 0.95), and sum of the four bands (r 2 = 0.94); however, the slopes and intercepts for these relationships were different. The 52 spectral curves yielded nine cluster groups, which mostly related to the Munsell Soil Color value and Soil albedo Soil characteristics. The 0.3- to 2.8-μm albedos of smoothed Soils can be accurately estimated using the regression relationship: Soil albedo (0.3-2.8 μm) = 0.069 (Color value) - 0.114. Using the regression equations presented here, spectral reflectance data in selected visible and NIR bands can also be used to predict albedo.
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Predicting Soil Albedo from Soil Color and Spectral Reflectance Data
Soil Science Society of America Journal, 2000Co-Authors: Donald F. Post, A. Fimbres, A.d. Matthias, E.e. Sano, L. Accioly, A.k. Batchily, L G FerreiraAbstract:The albedo of earth surface features, such as Soil, is an important component of models that define land-surface meteorological processes. If land surfaces have no vegetative cover, Soil properties determine the amount of solar radiation absorbed or reflected. We evaluated the influence of two Soil properties, Soil Color and Soil moisture, on Soil albedo. Two Soil moisture conditions were studied, air dry and wet, defined as the condition when the water films are absorbed by the Soil and no water glistens on the Soil surface. The albedos for 26 U.S. Soils were measured with an Eppley pyranometer, which integrates radiant energy in wavelengths between 0.3 to 2.8 μm. Soil Colors were measured with a Minolta Chroma Meter and spectral reflectance curves from 0.45 to 0.9 μm (measured in 0.1-μm increments) were determined with a multispectral radiometer. All measurements were made on
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Predicting Soil Albedo from Soil Color and Spectral Reflectance Data
Soil Science Society of America Journal, 2000Co-Authors: Donald F. Post, A. Fimbres, A.d. Matthias, E.e. Sano, L. Accioly, A.k. Batchily, L G FerreiraAbstract:The albedo of earth surface features, such as Soil, is an important component of models that define land-surface meteorological processes. If land surfaces have no vegetative cover, Soil properties determine the amount of solar radiation absorbed or reflected. We evaluated the influence of two Soil properties, Soil Color and Soil moisture, on Soil albedo. Two Soil moisture conditions were studied, air dry and wet, defined as the condition when the water films are absorbed by the Soil and no water glistens on the Soil surface. The albedos for 26 U.S. Soils were measured with an Eppley pyranometer, which integrates radiant energy in wavelengths between 0.3 to 2.8 {micro}m. Soil Colors were measured with a Minolta Chroma Meter and spectral reflectance curves from 0.45 to 0.9 {micro}m (measured in 0.1-{micro}m increments) were determined with a multispectral radiometer. All measurements were made on , green [IMG]f3.gif" BORDER="0"> , red [IMG]f1.gif" BORDER="0"> , near infrared (NIR), [IMG]f4.gif" BORDER="0"> , and sum of the four bands [IMG]f5.gif" BORDER="0"> ; however, the slopes and intercepts for these relationships were different. The 52 spectral curves yielded nine cluster groups, which mostly related to the Munsell Soil Color value and Soil albedo Soil characteristics. The 0.3- to 2.8-{micro}m albedos of smoothed Soils can be accurately estimated using the regression relationship: Soil albedo (0.3-2.8 {micro}m) = 0.069 (Color value) - 0.114. Using the regression equations presented here, spectral reflectance data in selected visible and NIR bands can also be used to predict albedo.
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relationship between buffelgrass survival organic carbon and Soil Color in mexico
Soil Science Society of America Journal, 1995Co-Authors: Fernando A Ibarraf, Donald F. Post, Martha H Martinr, Todd A Crowl, Raymond W Miller, Allen G RasmussenAbstract:T-4464 buffelgrass (Cenchrus ciliaris L.), a perennial bunchgrass from Africa, has been extensively seeded throughout Mexico. After establishment and grazing, T-4464 either persists with time and actively invades surrounding areas (spreads), persists with time but does not increase (persists), or declines with time and all plants die (dies). To help land managers select high-potential seeding sites, we classified 139 seeding sites in three survival regimes : (i) spreads, (ii) persists, and (iii) dies. In previous research, we identified a relationship between plant survival and organic C. This research was designed to identify relationships between organic C and Soil Color. Single comparisons between organic C and Munsell hue, value, chroma, and reflectance in dry and moist Soils were poor predictors of plant survival. To predict buffelgrass survival among the three survival regimes and between spreads and dies, we used discriminant function analyses. In dry Soil, a model including value and chroma correctly classified 53% (Wilke's λ = 0.8) of the seeding sites in the three survival regimes, while in moist Soils, value and reflectance components correctly classified 61% (Wilke's λ = 0.7) of the seeding sites. A dry Soil model including value, chroma, and reflectance correctly classified 81% (Wilke's λ = 0.7) of the seeding sites between spreads and dies, while a moist Soil model, including the same components, correctly classified 83% (Wilke's λ = 0.6) of the seeding sites. Survival regime selection with multiple Soil Color components prior to brush control and sowing will reduce adverse economic and environmental consequences and enhance long-term beef production.
L G Ferreira - One of the best experts on this subject based on the ideXlab platform.
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Predicting Soil Albedo from Soil Color and Spectral Reflectance Data
Soil Science Society of America Journal, 2000Co-Authors: Donald F. Post, A. Fimbres, A.d. Matthias, E.e. Sano, L. Accioly, A.k. Batchily, L G FerreiraAbstract:The albedo of earth surface features, such as Soil, is an important component of models that define land-surface meteorological processes. If land surfaces have no vegetative cover, Soil properties determine the amount of solar radiation absorbed or reflected. We evaluated the influence of two Soil properties, Soil Color and Soil moisture, on Soil albedo. Two Soil moisture conditions were studied, air dry and wet, defined as the condition when the water films are absorbed by the Soil and no water glistens on the Soil surface. The albedos for 26 U.S. Soils were measured with an Eppley pyranometer, which integrates radiant energy in wavelengths between 0.3 to 2.8 μm. Soil Colors were measured with a Minolta Chroma Meter and spectral reflectance curves from 0.45 to 0.9 μm (measured in 0.1-μm increments) were determined with a multispectral radiometer. All measurements were made on
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predicting Soil albedo from Soil Color and spectral reflectance data
Soil Science Society of America Journal, 2000Co-Authors: Donald F. Post, A. Fimbres, A.d. Matthias, E.e. Sano, L. Accioly, A.k. Batchily, L G FerreiraAbstract:The albedo of earth surface features, such as Soil, is an important component of models that define land-surface meteorological processes. If land surfaces have no vegetative cover, Soil properties determine the amount of solar radiation absorbed or reflected. We evaluated the influence of two Soil properties, Soil Color and Soil moisture, on Soil albedo. Two Soil moisture conditions were studied, air dry and wet, defined as the condition when the water films are absorbed by the Soil and no water glistens on the Soil surface. The albedos for 26 U.S. Soils were measured with an Eppley pyranometer, which integrates radiant energy in wavelengths between 0.3 to 2.8 μm. Soil Colors were measured with a Minolta Chroma Meter and spectral reflectance curves from 0.45 to 0.9 μm (measured in 0.1-μm increments) were determined with a multispectral radiometer. All measurements were made on <2-mm smooth Soil surfaces, and the dry and wet data were combined for statistical analyses. Soil albedos were significantly correlated with Munsell Soil Color value (r 2 = 0.93), blue (r 2 = 0.82), green (r 2 = 0.90), red (r 2 = 0.93), near infrared (NIR), (r 2 = 0.95), and sum of the four bands (r 2 = 0.94); however, the slopes and intercepts for these relationships were different. The 52 spectral curves yielded nine cluster groups, which mostly related to the Munsell Soil Color value and Soil albedo Soil characteristics. The 0.3- to 2.8-μm albedos of smoothed Soils can be accurately estimated using the regression relationship: Soil albedo (0.3-2.8 μm) = 0.069 (Color value) - 0.114. Using the regression equations presented here, spectral reflectance data in selected visible and NIR bands can also be used to predict albedo.
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Predicting Soil Albedo from Soil Color and Spectral Reflectance Data
Soil Science Society of America Journal, 2000Co-Authors: Donald F. Post, A. Fimbres, A.d. Matthias, E.e. Sano, L. Accioly, A.k. Batchily, L G FerreiraAbstract:The albedo of earth surface features, such as Soil, is an important component of models that define land-surface meteorological processes. If land surfaces have no vegetative cover, Soil properties determine the amount of solar radiation absorbed or reflected. We evaluated the influence of two Soil properties, Soil Color and Soil moisture, on Soil albedo. Two Soil moisture conditions were studied, air dry and wet, defined as the condition when the water films are absorbed by the Soil and no water glistens on the Soil surface. The albedos for 26 U.S. Soils were measured with an Eppley pyranometer, which integrates radiant energy in wavelengths between 0.3 to 2.8 {micro}m. Soil Colors were measured with a Minolta Chroma Meter and spectral reflectance curves from 0.45 to 0.9 {micro}m (measured in 0.1-{micro}m increments) were determined with a multispectral radiometer. All measurements were made on , green [IMG]f3.gif" BORDER="0"> , red [IMG]f1.gif" BORDER="0"> , near infrared (NIR), [IMG]f4.gif" BORDER="0"> , and sum of the four bands [IMG]f5.gif" BORDER="0"> ; however, the slopes and intercepts for these relationships were different. The 52 spectral curves yielded nine cluster groups, which mostly related to the Munsell Soil Color value and Soil albedo Soil characteristics. The 0.3- to 2.8-{micro}m albedos of smoothed Soils can be accurately estimated using the regression relationship: Soil albedo (0.3-2.8 {micro}m) = 0.069 (Color value) - 0.114. Using the regression equations presented here, spectral reflectance data in selected visible and NIR bands can also be used to predict albedo.
D G Westfall - One of the best experts on this subject based on the ideXlab platform.
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normalized difference vegetation index and Soil Color based management zones in irrigated maize
Agronomy Journal, 2008Co-Authors: Daniel Inman, R Khosla, Robin M Reich, D G WestfallAbstract:Spectral vegetation indices such as the normalized difference vegetation index (NDVI) have been shown to be useful for indirectly obtaining crop information such as photosynthetic efficiency, productivity potential, and potential yield. The objectives of this study were (i) to examine the relationships among NDVI determined early in the growing season, Soil Color-based management zones (SCMZ), and relative maize (Zea mays L.) grain yield and (ii) to determine if coupling Soil Color-based management zones with NDVI improves the accuracy of Soil Color-based management zone precision crop management strategy. Remotely sensed imagery was acquired by aircraft at approximately the eight-leaf crop growth stage (V8). Kappa statistics and percent areal agreement suggested a slight to substantial areal association among NDVI and relative grain yield (K = 0.10 to 0.63; % areal agreement = 13-67). Regression models were variable and explained among 25 to 82% of the variability in relative grain yield. Inclusion of Soil Color-based management zones in the regression models resulted in marginal improvements. When the affects of Soil Color-based management zones were removed, NDVI accounted for among 10 to 47% of the variability. The NDVI determined early does have potential to be useful in irrigated maize cropping systems. Coupling NDVI and SCMZs did not bring additional benefits to our Soil Color-based management zone strategy.
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Normalized Difference Vegetation Index and Soil Color‐Based Management Zones in Irrigated Maize
Agronomy Journal, 2008Co-Authors: Daniel Inman, R Khosla, Robin M Reich, D G WestfallAbstract:Spectral vegetation indices such as the normalized difference vegetation index (NDVI) have been shown to be useful for indirectly obtaining crop information such as photosynthetic efficiency, productivity potential, and potential yield. The objectives of this study were (i) to examine the relationships among NDVI determined early in the growing season, Soil Color-based management zones (SCMZ), and relative maize (Zea mays L.) grain yield and (ii) to determine if coupling Soil Color-based management zones with NDVI improves the accuracy of Soil Color-based management zone precision crop management strategy. Remotely sensed imagery was acquired by aircraft at approximately the eight-leaf crop growth stage (V8). Kappa statistics and percent areal agreement suggested a slight to substantial areal association among NDVI and relative grain yield (K = 0.10 to 0.63; % areal agreement = 13-67). Regression models were variable and explained among 25 to 82% of the variability in relative grain yield. Inclusion of Soil Color-based management zones in the regression models resulted in marginal improvements. When the affects of Soil Color-based management zones were removed, NDVI accounted for among 10 to 47% of the variability. The NDVI determined early does have potential to be useful in irrigated maize cropping systems. Coupling NDVI and SCMZs did not bring additional benefits to our Soil Color-based management zone strategy.
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evaluating Soil Color with farmer input and apparent Soil electrical conductivity for management zone delineation
Agronomy Journal, 2004Co-Authors: K L Fleming, Dale F Heermann, D G WestfallAbstract:Variable rate fertilizer application technology (VRT) can provide an opportunity to more efficiently utilize fertilizer inputs; however, accurate prescription maps are essential. Researchers and farmers have understood the value of dividing whole fields into smaller, homogeneous regions or management zones for fertility management. Management zones can be defined as spatially homogeneous subregions within a field that have similar crop input needs. Delineating management zones that characterize the spatial variability within a field may provide effective prescription, maps for VRT. The objective of this research was to compare and evaluate management zones developed from Soil Color (SC) and farmer experience with management zones developed from apparent electrical conductivity (EC a ). These two methods of developing management zones were compared with Soil nutrient levels, texture, and crop yields collected on two fields in 1997. The Soil and yield parameters followed the trends indicated by both management zone methods at Field 1 with the highest values found in the high productivity zones and the lowest the low productivity zones. Significant differences were found among the management zones. However, at Field 2 the high and medium productivity zones were generally not significantly different using the SC approach, whereas the EC a approach was effective in identifying three distinct management zones. Both methods of developing management zones seem to be identifying homogeneous subregions within fields.
Peter Leinweber - One of the best experts on this subject based on the ideXlab platform.
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rapid assessment of Soil organic matter Soil Color analysis and fourier transform infrared spectroscopy
Geoderma, 2016Co-Authors: Karen Baumann, Ingo Schoning, Marion Schrumpf, Ruth H Ellerbrock, Peter LeinweberAbstract:Abstract Soil organic matter (SOM) content and composition may be affected by geographic region, land use (grassland vs. forest) and management intensity (intensive vs. extensive). To asses these effects SOM of 300 German Soils was characterized using Soil Color analyses (L*,a*,b*-values) and Fourier transform infrared (FTIR) spectroscopy. Soil lightness (L*-value) was strongly negatively correlated with the Soil organic carbon content and this relationship was stronger when the previously sieved Soils were ground. Using the band at wavenumber 1634 cm − 1 (as determined by FTIR) as a proxy for aromaticity of SOM the L*-value was negatively correlated with aromaticity. Geographic region as well as land use affected L*-, a*- and b*-values. FTIR results suggested that particularly amides and polysaccharides were affected by geographic region, while mainly polysaccharides were affected by land use. We conclude that Soil Color analysis can provide additional information on environmental circumstances/site effects which may affect SOM composition. A few only weak correlations of Soil Color/SOM composition parameters with management intensity indicate that either changes in SOM parameters were too small or that the applied management indices were not sensitive enough to management effects.
Richard Escadafal - One of the best experts on this subject based on the ideXlab platform.
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Relationships between satellite-based radiometric indices simulated using laboratory reflectance data and typic Soil Color of an arid environment
Remote Sensing of Environment, 1998Co-Authors: Renaud Mathieu, Marcel Pouget, Bernard Cervelle, Richard EscadafalAbstract:By definition, the Color of an object such a Soil is highly dependent on its reflectance properties in the visible spectrum. In this study, the relationships between Soil Color and simulated reflectance values for the Landsat TM and SPOT HRV satellites are examined from a laboratory standpoint. Visible reflectance spectra were acquired for 124 Soil samples originated from an arid environment, and selected radiometric indices were worked out for both sensors. All the earlier studies relative to Soil Color and remote sensing have considered the widely known Munsell method as a reference for Soil Color quantification. Some characteristics of this system based on a visual comparison of a Soil sample with painted Color chips may complicate the establishment of simple relationships between reflectance data and Soil Color. We have applied the CIE 1931 standard method of Color measurement which consists in computing Color parameters directly from reflectance spectra using Colorimetric equations. Color data are expressed according to two polar coordinates called Helmholtz coordinates (dominant wavelength and purity of excitation) and luminance variable having a similar meaning to the Munsell hue, chroma, and value, respectively. The Munsell system is also employed to estimate Soil Color. Linear regression analysis between Soil Color and radiometric indices show a systematic improvement of correlations (r) from about 0.7 to more than 0.9 using Munsell data and Helmoltz data, respectively. Simple radiometric indices (band combinations) calculated from broad blue, green, and red bands are found to be good predictors of each of the Soil Color components. The increasing availability of spectroradiometers', including in the field, should stimulate the use of Helmholtz coordinates, as a beneficial alternative to the Munsell chart to obtain a precise and reproducible Color quantification which may be useful for remote sensing applications. The radiometric indices utilized in this study are potentially helpful to contribute to Soil resource and Soil degradation cartography using visible satellite data in vast arid regions where Soil data are not readily available.
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remote sensing of Soil Color principles and applications
Remote Sensing Reviews, 1993Co-Authors: Richard EscadafalAbstract:Abstract Color is an important parameter in Soil Science, widely used since the very first Soil investigations. Color visually estimated in the field with the Munsell Atlas is used for Soil identification and classification, for estimating various Soil properties and in Soil survey and mapping. The difficulties encountered in the first attempts in establishing empirical relationships between surface Color and remote sensing data led to use a more scientific approach. In this paper the significance of Munsell Soil Color versus spectral signature is investigated and discussed with the help of Colorimetric concepts and techniques. Different experiments based on field and laboratory spectral reflectance measurements are summarized. Among the results, the transformation of Munsell data into Red, Green, Blue Color coordinates (R,G,B) is the basis for a pragmatic, physically based model relating surface Color to satellite data in visible bands. Applications of this model to satellite imagery for Soil mapping, so...