Wavelength-Dependent Seeing Systematically Changes the Normalized Slope of Telescopic Reflectance Spectra of Mercury

Telescopic observations of Mercury consistently report systematic variations of the normalized spectral slope of visible-to-near-infrared reflectance spectra. This effect was previously assumed to be a photometric property of the regolith, but it is not yet fully understood. After the MESSENGER mission, detailed global spectral maps of Mercury are available that better constrain Mercury’s photometry. So far, wavelength-dependent seeing has not been considered in the context of telescopic observations of Mercury. This study investigates the effect of wavelength-dependent seeing on systematic variations of Mercury’s normalized spectral reflectance slope. Therefore, we simulate the disk of Mercury for an idealized scenario, as seen by four different telescopic campaigns using the Hapke and the Kaasalainen–Shkuratov photometric model, the MDIS global mosaic, and a simple wavelength-dependent seeing model. The simulation results are compared with the observations of previous telescopic studies. We find that wavelength-dependent seeing affects the normalized spectral slope in several ways. The normalized slopes are enhanced near the limb, decrease toward the rim of the seeing disk, and even become negative. The decrease of the normalized spectral slope is consistent with previous observations. However, previous studies have associated the spectral slope variations with photometric effects that correlate with the emission angle. Our study suggests that wavelength-dependent seeing may cause these systematic variations. The combined reflectance and seeing model can also account for slope variations between different measurement campaigns. We report no qualitative differences between results based on the Hapke model or the Kaasalainen–Shkuratov model.

The use of near-infrared reflectance spectroscopy (NIRS) to predict dairy fibre feeds in vitro digestibility

Information on dairy fibre feed digestibility is important in ration formulation to better predict dairy cattle performance. However, its measurement takes time. Near-infrared reflectance spectroscopy (NIRS) is a rapid, precise, and cost-effective method to predict nutrient value, such as chemical content and digestibility of feedstuffs. This study aims to develop a database for an in vitro digestibility prediction model using NIRS, including dry matter digestibility (DMD), neutral and acid detergent fibre digestibility (NDFD and ADFD), and hemicellulose digestibility (HSD). Eighty dietary fibre feeds consisting of Napier grass, natural grass, rice straw, corn stover, and corn-husk were collected from four dairy farming areas in West Java (Cibungbulang District of Bogor Regency, Parung Kuda District of Sukabumi Regency, Pangalengan District of Bandung Regency, and Lembang District of West Bandung Regency). The spectrum for each sample was collected thrice using NIRSflex 500, which was automatically separated by 2/3 for calibration and 1/3 for validation. External validation was conducted by measuring 20 independent samples. Calibration and validation models were carried out by NIRCal V5.6 using the partial least squares (PLS) regression. The results showed that all parameters produce r2 > 0.5 except for ADFD. Relative prediction deviation (RPD) > 1.5 was only found in hemicellulose digestibility prediction. RPL (SEP/SEL) <1.0 were found in DMD and hemicellulose digestibility. It is concluded that hemicellulose digestibility can be predicted using NIRS accurately while other parameters need improvement.