Estimation of soil and vegetation temperatures with multiangular thermal infrared observations: IMGRASS, HEIFE, and SGP 1997 experiments

The potential of directional observations in the thermal infrared region for land surface studies is a largely uncharted area of research. The availability of the dual-view Along Track Scanning Radiometer (ATSR) observations led to explore new opportunities in this direction. In the context of studies on heat transfer at heterogeneous land surfaces, multiangular thermal infrared (TIR) observations offer the opportunity of overcoming fundamental difficulties in modeling sparse canopies. Three case studies were performed on the estimation of the component temperatures of foliage and soil. The first one included the use of multi-temporal field measurements at view angles of 0°, 23° and 52°. The second and third one were done with directional ATSR observations at view angles of 0° and 53° only. The first one was a contribution to the Inner-Mongolia Grassland Atmosphere Surface Study (IMGRASS) experiment in China, the second to the Hei He International Field Experiment (HEIFE) in China and the third one to the Southern Great Plains 1997 (SGP 1997) experiment in Oklahoma, United States. The IMGRASS experiment provided useful insights on the applicability of a simple linear mixture model to the analysis of observed radiance. The HEIFE case study was focused on the large oasis of Zhang-Ye and led to useful estimates of soil and vegetation temperatures. The SGP 1997 contributed a better understanding of the impact of spatial heterogeneity on the accuracy of retrieved foliage and soil temperatures. Limitations in the approach due to varying radiative and boundary layer forcing and to the difference in spatial resolution between the forward and the nadir view are evaluated through a combination of modeling studies and analysis of field data

Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes

A

Determination of Soybean Oil, Protein and Amino Acid Residues in Soybean Seeds by High Resolution Nuclear Magnetic Resonance (NMRS) and Near Infrared (NIRS)

A detailed account is presented of our high resolution nuclear magnetic resonance (HR-NMR) and near infrared (NIR) calibration models, methodologies and validation procedures, together with a large number of composition analyses for soybean seeds. NIR calibrations were developed based on both HR-NMR and analytical chemistry reference data for oil and twelve amino acid residues in mature soybeans and soybean embryos. This is our first report of HR-NMR determinations of amino acid profiles of proteins from whole soybean seeds, without protein extraction from the seed. It was found that the best results for both oil and protein calibrations were obtained with a Partial Least Squares Regression (PLS-1) analysis of our extensive NIR spectral data, acquired with either a DA7000 Dual Diode Array (Si and InGaAs detectors) instrument or with several Fourier Transform NIR (FT-NIR) spectrometers equipped with an integrating sphere/InGaAs detector accessory. In order to extend the bulk soybean samples calibration models to the analysis of single soybean seeds, we have analized in detail the component NIR spectra of all major soybean constituents through spectral deconvolutions for bulk, single and powdered soybean seeds. Baseline variations and light scattering effects in the NIR spectra were corrected, respectively, by calculating the first-order derivatives of the spectra and the Multiplicative Scattering Correction (MSC). The single soybean seed NIR spectra are broadly similar to those of bulk whole soybeans, with the exception of minor peaks in single soybean NIR spectra in the region from 950 to 1,000 nm. Based on previous experience with bulk soybean NIR calibrations, the PLS-1 calibration model was selected for protein, oil and moisture calibrations that we developed for single soybean seed analysis. In order to improve the reliability and robustness of our calibrations with the PLS-1 model we employed standard samples with a wide range of soybean constituent compositions: from 34% to 55% for protein, from 11% to 22% for oil and from 2% to 16% for moisture. Such calibrations are characterized by low standard errors and high degrees of correlation for all major soybean constituents. Morever, we obtained highly resolved NIR chemical images for selected regions of mature soybean embryos that allow for the quantitation of oil and protein components. Recent developments in high-resolution FT-NIR microspectroscopy extend the NIR sensitivity range to the picogram level, with submicron spatial resolution in the component distribution throughout intact soybean seeds and embryos. Such developments are potentially important for biotechnology applications that require rapid and ultra- sensitive analyses, such as those concerned with high-content microarrays in Genomics and Proteomics research. Other important applications of FT-NIR microspectroscopy are envisaged in biomedical research aimed at cancer prevention, the early detection of tumors by NIR-fluorescence, and identification of single cancer cells, or single virus particles in vivo by super-resolution microscopy/ microspectroscopy

Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet

Leaf area index (LAI) and water content (WC) in the root zone are two major hydro-meteorological parameters that exhibit a dominant control on water, energy and carbon fluxes, and are therefore important for any regional eco-hydrological or climatological study. To investigate the potential for retrieving these parameter from hyperspectral remote sensing, we have investigated plant spectral reflectance (400-2,500 nm, ASD FieldSpec3) for two major agricultural crops (sugar beet and spring barley) in the mid-latitudes, treated under different water and nitrogen (N) conditions in a greenhouse experiment over the growing period of 2008. Along with the spectral response, we have measured soil water content and LAI for 15 intensive measurement campaigns spread over the growing season and could demonstrate a significant response of plant reflectance characteristics to variations in water content and nutrient conditions. Linear and non-linear dimensionality analysis suggests that the full band reflectance information is well represented by the set of 28 vegetation spectral indices (SI) and most of the variance is explained by three to a maximum of eight variables. Investigation of linear dependencies between LAI and soil WC and pre-selected SI's indicate that: (1) linear regression using single SI is not sufficient to describe plant/soil variables over the range of experimental conditions, however, some improvement can be seen knowing crop species beforehand; (2) the improvement is superior when applying multiple linear regression using three explanatory SI's approach. In addition to linear investigations, we applied the non-linear CART (Classification and Regression Trees) technique, which finally did not show the potential for any improvement in the retrieval process

Empirical modeling of the stellar spectrum of galaxies

An empirical method of modeling the stellar spectrum of galaxies is proposed, based on two successive applications of Principal Component Analysis (PCA). PCA is first applied to the newly available stellar library STELIB, supplemented by the J, H and K$_{s}$ magnitudes taken mainly from the 2 Micron All Sky Survey (2MASS). Next the resultant eigen-spectra are used to fit the observed spectra of a sample of 1016 galaxies selected from the Sloan Digital Sky Survey Data Release One (SDSS DR1). PCA is again applied, to the fitted spectra to construct the eigen-spectra of galaxies with zero velocity dispersion. The first 9 galactic eigen-spectra so obtained are then used to model the stellar spectrum of the galaxies in SDSS DR1, and synchronously to estimate the stellar velocity dispersion, the spectral type, the near-infrared SED, and the average reddening. Extensive tests show that the spectra of different type galaxies can be modeled quite accurately using these eigen-spectra. The method can yield stellar velocity dispersion with accuracies better than 10%, for the spectra of typical S/N ratios in SDSS DR1.Comment: 34 pages with 18 figures, submitted to A

Optical constants of silicon carbide for astrophysical applications. II. Extending optical functions from IR to UV using single-crystal absorption spectra

Laboratory measurements of unpolarized and polarized absorption spectra of various samples and crystal stuctures of silicon carbide (SiC) are presented from 1200--35,000 cm$^{-1}$ ($\lambda \sim$ 8--0.28 $\mu$m) and used to improve the accuracy of optical functions ($n$ and $k$) from the infrared (IR) to the ultraviolet (UV). Comparison with previous $\lambda \sim$ 6--20 $\mu$m thin-film spectra constrains the thickness of the films and verifies that recent IR reflectivity data provide correct values for $k$ in the IR region. We extract $n$ and $k$ needed for radiative transfer models using a new ``difference method'', which utilizes transmission spectra measured from two SiC single-crystals with different thicknesses. This method is ideal for near-IR to visible regions where absorbance and reflectance are low and can be applied to any material. Comparing our results with previous UV measurements of SiC, we distinguish between chemical and structural effects at high frequency. We find that for all spectral regions, 3C ($\beta$-SiC) and the $\vec{E}\bot \vec{c}$ polarization of 6H (a type of $\alpha$-SiC) have almost identical optical functions that can be substituted for each other in modeling astronomical environments. Optical functions for $\vec{E} \| \vec{c}$ of 6H SiC have peaks shifted to lower frequency, permitting identification of this structure below $\lambda \sim4\mu$m. The onset of strong UV absorption for pure SiC occurs near 0.2 $\mu$m, but the presence of impurities redshifts the rise to 0.33 $\mu$m. Optical functions are similarly impacted. Such large differences in spectral characteristics due to structural and chemical effects should be observable and provide a means to distinguish chemical variation of SiC dust in space.Comment: 46 pages inc. 8 figures and 2 full tables. Also 6 electronic-only data files. Accepted by Ap