Sample collection and measurement of soil bulk density (BD) are often labor-intensive and expensive in large regions. Conversely, soil spectra are easy to measure and facilitate BD prediction. However, the literature suggests that the damage to the physical structure of soil during scanning spectra on the ground and/or sieved samples might hinder the capacity of spectral technology to accurately predict BD. In addition, because some soil properties that have high correlations with BD, such as the soil organic matter (SOM), are routinely measured and available in most soil databases, coupling them with soil spectra may improve BD prediction compared to using soil properties or spectra. Therefore, in this study, we propose a novel spectral pedo-transfer function (spectral PTF) that couples the measured visible and near-infrared spectra of soils on intact samples and other soil properties to accurately predict the BD (BD = f (soil spectra, soil properties)), which is different from the traditional PTF that uses only soil properties (BD = f (soil properties)) or spectra alone (BD = f (soil spectra)). In this study, we collected topsoil (0-20 cm) and subsoil (20-40 cm) samples from 586 sites in Northeast China, covering a large area of 1.09 million km(2) characterized by black soils with high SOM contents. Five routinely measured soil properties were selected: SOM, moisture content (MC), Sand, Silt, and Clay, and various spectral PTFs with one, two, and three soil properties were calibrated using the partial least square regression. The cross-validation results show that the traditional PTF can only predict BD for subsoil with an R-2 of 0.51 and an RMSE of 0.11 g center dot cm(-3) when using SOM + MC + Silt or SOM + MC. Compared to subsoil, topsoil and all layers (topsoil + subsoil) had a lower BD prediction accuracy, and a saturation effect was observed for BD values above 1.5 g center dot cm(-3). Unexpectedly, the soil spectra did not provide a higher BD prediction accuracy than traditional PTFs, although the spectra were measured on intact samples. However, adding soil properties to the spectral PTF improved the prediction accuracy and saturation effect for high BD values. The optimal spectral PTF with a single soil property (MC) showed an acceptable BD prediction performance with R-2 >= 0.49, RPD>1.4, and RPIQ>1.7 regardless of whether the sample was topsoil, subsoil, or all layers. Furthermore, the spectral PTF with two or three soil properties yielded a slightly better prediction performance and a more stable prediction among different combinations of soil properties. These results indicate that soil properties and spectra are irreplaceable for BD prediction. Our study demonstrates the potential of spectral PTFs for the accurate prediction of BD and offers insights into the prediction of other soil properties using soil spectra.

Knowledge of the occurrence of water in the solar system provides key information concerning the formation and evolution of the solar system and lifeforms. In recent years, multiple remote-sensing observations have suggested the existence of water ice in permanently shadowed regions on some inner solar system bodies; however, the exact amount of water ice is highly uncertain. To test the performance of ice detection equipment for future lunar polar exploration missions, we constructed an apparatus to produce minute amounts of water ice (0.1-2 wt%) on lunar regolith analog minerals and measured their near-infrared spectra. The relationship between the strength of water absorption and the water content was quantified using the absorption at 1.5 mu m in the reflectance spectra. The results show that the detectability of water ice attached to mineral grains depends on the mineral species. Laboratory reflectance spectra were compared to Hapke model spectra, and the observed spectral feature similarities indicate that the Hapke model can be effectively used when the ice is mixed in the form of spherical grains.

Water ice may be allowed to accumulate in permanently shaded regions on airless bodies in the inner solar system such as Mercury, the Moon, and Ceres [Watson K, et al. (1961) J Geophys Res 66: 3033-3045]. Unlike Mercury and Ceres, direct evidence for water ice exposed at the lunar surface has remained elusive. We utilize indirect lighting in regions of permanent shadow to report the detection of diagnostic near-infrared absorption features of water ice in reflectance spectra acquired by the Moon Mineralogy Mapper [M (3)] instrument. Several thousand M (3) pixels (similar to 280 x 280 m) with signatures of water ice at the optical surface (depth of less than a few millimeters) are identified within 20 degrees latitude of both poles, including locations where independent measurements have suggested that water ice may be present. Most ice locations detected in M (3) data also exhibit lunar orbiter laser altimeter reflectance values and Lyman Alpha Mapping Project instrument UV ratio values consistent with the presence of water ice and also exhibit annual maximum temperatures below 110 K. However, only similar to 3.5% of cold traps exhibit ice exposures. Spectral modeling shows that some ice-bearing pixels may contain similar to 30 wt % ice that is intimately mixed with dry regolith. The patchy distribution and low abundance of lunar surface-exposed water ice might be associated with the true polar wander and impact gardening. The observation of spectral features of H2O confirms that water ice is trapped and accumulates in permanently shadowed regions of the Moon, and in some locations, it is exposed at the modern optical surface.

The European SMART-1 mission to the Moon, primarily a testbed for innovative technologies, was launched in September 2003 and will reach the Moon in 2005. On board are several scientific instruments, including thepoint-spectrometer SMART-1 Infrared Spectrometer (SIR). Taking into account the capabilities of the SMART-I mission and the SIR instrument in particular, as well as the open questions ill lunar science, a selection of targets for SIR observations has been compiled. SIR can address at least five topics: (1) Surface/regolith processes; (2) Lunar volcanism; (3) Lunar crust structure; (4) Search for spectral signatures of ices at the lunar poles; and (5) Ground truth and study of geometric effects on the spectral shape. For each topic we will discuss specific observation modes, necessary to achieve our scientific goals. The majority of SIR targets will be observed in the nadir-tracking mode. More than 100 targets, which require off-nadir pointing and off-nadir tracking, are planned. h is expected that results of SIR observations will significantly increase our understanding of the Moon. Since the exact arrival date and the orbital parameters of the SMART-I spacecraft are not known yet, a more detailed planning of the scientific observations will follow in the near future. (C) 2004 Elsevier Ltd. All rights reserved.