scholarly journals Spectroscopic Imaging of Solid Planetary Surfaces

1995 ◽  
Vol 149 ◽  
pp. 294-297
Author(s):  
P.C. Pinet
Keyword(s):  
Remote Sensing ◽  
Spectroscopic Data ◽  
Hyperspectral Data ◽  
Solid Surfaces ◽  
Spectroscopic Techniques ◽  
Planetary Surfaces ◽  
Spectral Variability ◽  
Mixture Modelling ◽  
Spectral Mixture ◽  
Orbital Imaging

Seen from Sirius through the eye of the telescope, our inner solar system would easily fit within one CCD-pixel. The purpose of the present paper is: i) to provide with a general overview of the use of imaging or 3D-spectroscopy for the study of the solid planetary surfaces, ii) to demonstrate that the analysis of 3D spectroscopic data on the basis of spectral mixture modelling permits to describe the subpixel spectral variability related to mineralogy of the planetary solid surfaces. In the following, a few cases are discussed concerning the remote sensing investigation in the UV-VIS-nIR domain of the lunar, terrestrial and martian surfaces, documented by means of multispectral or hyperspectral data, produced by telescopic, airborne or orbital imaging spectroscopic techniques.

Spectral Mixture for Remote Sensing

2019 ◽  
Author(s):  
Yosio Edemir Shimabukuro ◽  
Flávio Jorge Ponzoni
Keyword(s):  
Remote Sensing ◽  
Spectral Mixture

scholarly journals Measuring the Morphology and Dynamics of the Snake River by Remote Sensing

2011 ◽  
Vol 33 ◽  
pp. 3-15
Author(s):  
Carl Legleiter
Keyword(s):  
Remote Sensing ◽  
Image Data ◽  
River System ◽  
Field Work ◽  
Hyperspectral Data ◽  
Snake River ◽  
Central Component ◽  
Data Sets ◽  
Channel Form ◽  
And Behavior

The Snake River is a central component of Grand Teton National Park, and this dynamic fluvial system plays a key role in shaping the landscape and creating diverse aquatic and terrestrial habitat. The river’s complexity and propensity for change make effective characterization of this resource difficult, however, and conventional, ground-based methods are simply inadequate. Remote sensing provides an appealing alternative approach that could facilitate resource management while providing novel insight on the factors controlling channel form and behavior. In this study, we evaluate the potential to measure the morphology and dynamics of a large, complex river system such as the Snake using optical image data. Initially, we made use of existing, publicly available images and basic digital aerial photography acquired in August 2010. Analysis to date has focused on estimating flow depths from these data, and preliminary results indicate that remote bathymetric mapping is feasible but not highly accurate, with important constraints related to the limited radiometric resolution of these data sets. Additional, more sophisticated hyperspectral data are scheduled for collection in 2011, along with further field work.

scholarly journals HYPERSPECTRAL REMOTE SENSING ROLE IN ENHANCING CROP MAPPING: A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS

2019 ◽  
Vol 1 (1) ◽  
pp. 25-37
Author(s):  
Mohamad M. Awad
Keyword(s):  
Remote Sensing ◽  
Eastern Mediterranean ◽  
Hyperspectral Data ◽  
Spectral Angle Mapper ◽  
New Approach ◽  
Spectral Signatures ◽  
Agriculture Sector ◽  
Supervised Segmentation ◽  
Eastern Mediterranean Basin ◽  
Crop Mapping

In agriculture sector there is need for cheap, fast, and accurate data and technologies to help decision makers to find solutions for many agricultural problems. Many solutions depend significantly on the accuracy and efficiency of the crop mapping and crop yield estimation processes. High resolution spectral remote sensing can improve substantially crop mapping by reducing similarities between different crop types which has similar ecological conditions. This paper presents a new approach of combining a new tool, hyperspectral images and technologies to enhance crop mapping.  The tool includes spectral signatures database for the major crops in the Eastern Mediterranean Basin and other important metadata and processing functions. To prove the efficiency of the new approach, major crops such as “winter wheat” and “spring potato” are mapped using the spectral signatures database in the new tool, three different supervised algorithms, and CHRIS-Proba hyperspectral satellite images. The evaluation of the results showed that deploying different hyperspectral data and technologies can improve crop mapping. The improvements can be noticed with the increase of the accuracy to more than 86% with the use of the supervised algorithm Spectral Angle Mapper (SAM).

Remote sensing of fluorescence in inland waters: improvements from using hyperspectral data

2021 ◽  
Author(s):  
Remika S. Gupana ◽  
Daniel Odermatt ◽  
Abolfazl Irani Rahaghi ◽  
Camille Minaudo ◽  
Alexander Damm
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Data ◽  
Inland Waters

scholarly journals Utilizing Hyperspectral Remote Sensing for Soil Gradation

2020 ◽  
Vol 12 (20) ◽  
pp. 3312
Author(s):  
Jordan Ewing ◽  
Thomas Oommen ◽  
Paramsothy Jayakumar ◽  
Russell Alger
Keyword(s):  
Remote Sensing ◽  
Fine Particles ◽  
Soil Mechanics ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Controlled Environment ◽  
Sieve Analysis ◽  
Field Sample ◽  
Specific Objective ◽  
Soil Gradation

Soil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation. The specific objective of this work is to explore the application of hyperspectral remote sensing to be used as an alternative to traditional soil gradation estimation. The advantage of such an approach is that it would provide the soil gradation without having to obtain a field sample. This work will examine five different soil types from the Keweenaw Research Center within a laboratory-controlled environment for testing. Our study demonstrates a correlation between hyperspectral data, the percent gravel and sand composition of the soil. Using this correlation, one can predict the percent gravel and sand within a soil and, in turn, calculate the remaining percent of fine particles. This information can be vital to help identify the soil type, soil strength, permeability/hydraulic conductivity, and other properties that are correlated to the gradation of the soil.

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