Journal ArticleDOI
Estimation of Soil Properties by Orbital and Laboratory Reflectance Means and its Relation with Soil Classification
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TLDR
In this article, the spectral reflectance of ground area reflectance data from the TM-Landsat-5 image was used to estimate soil attributes by labora- tory and orbital sensors and compare these results with soil classification.Abstract:
Wet chemistry methods to extract soil properties such as Fe2O3, TiO2, MnO and clay are cost effective, time consuming and environmental polluter. Moreover, a large set of samples has to be collected for precise spatial mapping. Ordinary surface soil mapping is a problematic method. Accordingly, non destructive technologies, such as remote sens- ing methods can provide important vantages. The objective of the present work was to estimate soil attributes by labora- tory and orbital sensors and compare these results with soil classification. The study area is a 473 ha bare soil field located in the region of Barra Bonita, Brazil. A sampling grid of 100 by 100 m was established and the exact position of each point was georeferenced, and sent to traditional (wet) laboratory analyses. The soil samples reflectance were also acquired by a laboratory sensor using artificial illumination (450 to 2500 nm). Over the same selected ground area reflectance data were extracted from the TM-Landsat-5 image. Prediction equations between the satellite and laboratory reflectance data and the wet chemistry were generated for each attribute. Most of the generated equations presented high and significant R 2 such as for the Fe2O3 with 0.82 for laboratory and 0.67 for the orbital reflectance data. The comparison between reflec- tance estimates and laboratory wet measurements for iron presented 92.2% success for the laboratory and 91.3% for the orbital sensors. The comparison for the texture intervals, showed 65% and 50% success for laboratory and orbital data re- spectively. The iron contents obtained by the sensors allowed to better remotely classify soil classes. Soil extractions to determine these attributes can be substitute by spectral reflectance models based on the present methodology.read more
Citations
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Journal ArticleDOI
Using Imaging Spectroscopy to study soil properties
Eyal Ben-Dor,Sabine Chabrillat,José Alexandre Melo Demattê,G.R. Taylor,Joachim Hill,Michael L. Whiting,S. Sommer +6 more
TL;DR: In this paper, the authors provide an up-to-date overview of some of the case studies that have used IS technology for soil science applications, including soil degradation (salinity, erosion, and deposition), soil mapping and classification, soil genesis and formation, soil contamination, soil water content, and soil swelling.
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Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon
Fabio Castaldi,Angelo Palombo,Federico Santini,Simone Pascucci,Stefano Pignatti,Raffaele Casa +5 more
TL;DR: In this paper, the capabilities of seven multispectral and hyperspectral satellite imagers to estimate soil variables (clay, sand, silt and organic carbon content) were investigated using data from soil spectral libraries.
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Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions
TL;DR: In this article, the authors evaluate the efficiency of using data obtained from the Thematic Mapper (TM) sensor of Landsat 5 for digital soil mapping in a semi-arid region, based on multiple linear regression (MLR) and a random forest model (RFM).
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Spectral pedology: A new perspective on evaluation of soils along pedogenetic alterations
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Proximal spectral sensing to monitor phytoremediation of metal - contaminated soils
TL;DR: The theoretical basis whereby proximal spectral sensing of soil and vegetation could be used to monitor phytoremediation of metal-contaminated soils, and the eventual upscaling to imaging sensing is reviewed.
References
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Journal ArticleDOI
Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties
TL;DR: In this article, partial least squares regression (PLSR) was used to construct calibration models which were independently validated for the prediction of various soil properties from the soil spectra, including soil pHCa,p H w, lime requirement (LR), organic carbon (OC), clay, silt, sand, cation exchange capacity, exchangeable calcium (Ca), exchangeable aluminium (Al), nitrate-nitrogen (NO3-N), available phosphorus (PCol), exchangeability potassium (K) and electrical conductivity (EC).
Journal ArticleDOI
Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties
TL;DR: In this article, the authors evaluated the ability of NIRS to predict diverse soil properties, including total C, total N, moisture, cation-exchange capacity (CEC), 1.5 MPa water, basal respiration rate, sand, silt, and Mehlich III extractable Ca.
Reflectance spectroscopy - Quantitative analysis techniques for remote sensing applications. [in planetary surface geology]
R. N. Clark,T. L. Roush +1 more
TL;DR: In this article, the authors compared several methods for the analysis of remotely sensed reflectance data, including empirical methods and scattering theories, both of which are important for solving remote sensing problems.
Journal ArticleDOI
Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications
Roger N. Clark,Ted L. Roush +1 more
TL;DR: In this paper, it was shown that the mean optical path length in a particulate surface is in roughly inverse proportion to the square root of the absorption coefficient, and that absorption bands are Gaussians in shape when plotted as true absorptance vs photon energy, although they have a smaller intensity.
Journal ArticleDOI
Development of Reflectance Spectral Libraries for Characterization of Soil Properties
TL;DR: In this article, a scheme for development and use of soil spectral libraries for rapid nondestructive estimation of soil properties based on analysis of diffuse reflectance spectroscopy was developed. But the spectral library approach is not suitable for use in agricultural, environmental, and engineering applications.