10 resultados para Near Infrared
em Publishing Network for Geoscientific
Resumo:
As the length of marine cores increases and sampling intervals decrease, the need for rapid and inexpensive means of determining sediment composition has become apparent. In this study we examine one potentially useful technique for assessing compositional changes in marine cores, diffuse reflectance spectrophotometry. We examined near-ultraviolet, visible, and near-infrared reflectance spectra from five data sets. Each data set consists of calibration samples and test samples. The calibration samples' spectra were related to a sediment component using multiple linear regression. The resulting regression or calibration equations were then evaluated using the test samples. Calibration equations were written relating spectra to several sediment components incduding carbonate (Atlantic and east Pacific Rise ODP Site 847), organic carbon content (Atlantic and east Pacific Rise), and opal content (east Pacific Rise). The correlation coefficients for the regression equations ranged from a high of 0.99 for carbonate and opal at ODP Site 847 to a low of 0.97 for Atlantic carbonate indicating that spectral variations are highly correlated to sediment composition. All of the equations include a substantial number of variables from shorter visible and longer near ultraviolet wavelengths suggesting that these wavelengths are especially important for devices designed specifically to scan marine cores. Although equations for estimating organic and carbonate content appear independent of other sediment components, the opal equation is strongly dependent on carbonate content indicating that opal concentration is correlated to carbonate content. Tests of the calibration equations indicated that all our equations reasonably estimate the pattern of changes, either down core or in surface sediments. Where our spectral estimates have difficulty is with absolute values, frequently over or underestimating observed values by a substantial amount. Within these limitations diffuse reflectance spectrophotometry can be a useful tool for characterizing marine cores and as our understanding of the relationship between spectra and mineralogy improves so will estimates of absolute values.
Resumo:
Lake ice change is one of the sensitive indicators of regional and global climate change. Different sources of data are used in monitoring lake ice phenology nowadays. Visible and Near Infrared bands of imagery (VNIR) are well suited for the observation of freshwater ice change, for example data from AVHRR and MODIS. Active and passive microwave data are also used for the observation of lake ice, e.g., from satellite altimetry and radiometry, backscattering coefficient from QuickSCAT, brightness temperature (Tb) from SSM/I, SMMR, and AMSR-E. Most of the studies are about lake ice cover phenology, while few studies focus on lake ice thickness. For example, Hall et al. using 5 GHz (6 cm) radiometer data showed a good relationship between Tb and ice thickness. Kang et al. found the seasonal evolution of Tb at 10.65 GHz and 18.7 GHz from AMSR-E to be strongly influenced by ice thickness. Many studies on lake ice phenology have been carried out since the 1970s in cold regions, especially in Canada, the USA, Europe, the Arctic, and Antarctica. However, on the Tibetan Plateau, very little research has focused on lake ice-cover change; only a small number of published papers on Qinghai Lake ice observations. The main goal of this study is to investigate the change in lake ice phenology at Nam Co on the Tibetan Plateau using MODIS and AMSR-E data (monitoring the date of freeze onset, the formation of stable ice cover, first appearance of water, and the complete disappearance of ice) during the period 2000-2009.
Resumo:
During Ocean Drilling Program Leg 188 to Prydz Bay, East Antarctica, several of the shipboard scientists formed the High-Resolution Integrated Stratigraphy Committee (HiRISC). The committee was established in order to furnish an integrated data set from the Pliocene portion of Site 1165 as a contribution to the ongoing debate about Pliocene climate and climate evolution in Antarctica. The proxies determined in our various laboratories were the following: magnetostratigraphy and magnetic properties, grain-size distributions (granulometry), near-ultraviolet, visible, and near-infrared spectrophotometry, calcium carbonate content, characteristics of foraminifer, diatom, and radiolarian content, clay mineral composition, and stable isotopes. In addition to the HiRISC samples, other data sets contained in this report are subsets of much larger data sets. We included these subsets in order to provide the reader with a convenient integrated data set of Pliocene-Pleistocene strata from the East Antarctic continental margin. The data are presented in the form of 14 graphs (in addition to the site map). Text and figure captions guide the reader to the original data sets. Some preliminary interpretations are given at the end of the manuscript.
Resumo:
During Ocean Drilling Program Leg 199 in the equatorial Pacific, visible and near-infrared spectroscopy (VNIS) was used to measure the reflectance spectra (350-2500 nm) of 1343 sediment samples. Reflectance spectra were also measured for a suite of 60 samples of known mineralogy, thereby providing a local ground-truth calibration of spectral features to percentages of calcite, opal, smectite, and illite. The associated algorithm was used to calculate mineral percentages from the 1343 spectra. Using multiple regression and VNIS mineralogy, multisensor track physical properties and light spectroscopy data were then converted into continuous high-resolution mineralogy logs.
Resumo:
During Leg 138, we measured reflectance spectra in the visible and near-infrared bands (455-945 nm) every few centimeters on split core surfaces from eastern tropical Pacific Ocean sediments. Here, we evaluate predictions of the content of biogenic calcite, biogenic opal, and nonbiogenic sediments from the reflectance spectra. For Sites 844 through 847, which contain a significant nonbiogenic component, reflectance spectra yielded a useful proxy for the percentages of CaCO3 over a wide range of values from nearly 0% to 100%, with root-mean-square (RMS) errors of about 9%. Direct estimates of "nonbiogenic" sediment percentages, approximated by 100 - (CaCO3 + opal), were reasonably successful (RMS error of 10%), however, were incorrect in some intervals. This suggests that mineralogy of the nonbiogenic material changes through time and that further subdivision of this component will be needed for useful estimation from reflectance. For percentages of biogenic opal, calibration equations appear to work well (RMS error of 6%) at concentrations of less than 30%, but for higher opal concentrations, reflectance equations often underestimate the true contents of opal. Improvements in multiparameter lithologic estimates from reflectance spectra may come from (1) expanding the wavelengths measured to better capture unique mineral reflectance bands, and (2) adding the ability to measure diffuse, rather than directional, reflectance to minimize the effects of surface roughness.
Resumo:
Spectral albedo has been measured at Dome C since December 2012 in the visible and near infrared (400 - 1050 nm) at sub-hourly resolution using a home-made spectral radiometer. Superficial specific surface area (SSA) has been estimated by fitting the observed albedo spectra to the analytical Asymptotic Approximation Radiative Transfer theory (AART). The dataset includes fully-calibrated albedo and SSA that pass several quality checks as described in the companion article. Only data for solar zenith angles less than 75° have been included, which theoretically spans the period October-March. In addition, to correct for residual errors still affecting data after the calibration, especially at the solar zenith angles higher than 60°, we produced a higher quality albedo time-series as follows: In the SSA estimation process described in the companion paper, a scaling coefficient A between the observed albedo and the theoretical model predictions was introduced to cope with these errors. This coefficient thus provides a first order estimate of the residual error. By dividing the albedo by this coefficient, we produced the "scaled fully-calibrated albedo". We strongly recommend to use the latter for most applications because it generally remains in the physical range 0-1. The former albedo is provided for reference to the companion paper and because it does not depend on the SSA estimation process and its underlying assumptions.