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Canopy chlorophyll content (CCC) is an essential ecophysiological variable for photosynthetic functioning. Remote sensing of CCC is vital for a wide range of ecological and agricultural applications. The objectives of this study were to explore simple and robust algorithms for spectral assessment of CCC. Hyperspectral datasets for six vegetation types (rice, wheat, corn, soybean, sugar beet and natural grass) acquired in four locations (Japan, France, Italy and USA) were analysed. To explore the best predictive model, spectral index approaches using the entire wavebands and multivariable regression approaches were employed. The comprehensive analysis elucidated the accuracy, linearity, sensitivity and applicability of various spectral models. Multivariable regression models using many wavebands proved inferior in applicability to different datasets. A simple model using the ratio spectral index (RSI; R815, R704) with the reflectance at 815 and 704 nm showed the highest accuracy and applicability. Simulation analysis using a physically based reflectance model suggested the biophysical soundness of the results. The model would work as a robust algorithm for canopy-chlorophyll-metre and/or remote sensing of CCC in ecosystem and regional scales. The predictive-ability maps using hyperspectral data allow not only evaluation of the relative significance of wavebands in various sensors but also selection of the optimal wavelengths and effective bandwidths.

The physical properties of novel sulfur-containing biopolymers, poly[(3-hydroxybutyrate)-co-(3-mercaptopropionate)]s [P(3HB-co-3MP)s], have been investigated in detail by(1)H and (13)C NMR spectroscopy, wide-angle X-ray diffraction (WAXD) analysis, DSC, and FT-IR spectroscopy. Based on a solvent/non-solvent (chloroform/heptane) fractionation method, an original P(3HB-co-3MP) sample with 3MP unit content of 16.3 mol-% was fractionated into eight fractions with 3MP unit content ranging from 10.3 to 37.2 mol-% and number-average molecular weight from 0.4 x 10(5) to 2.9 x 10(5). The thermal and crystallization behavior were found to be greatly affected by the comonomer-unit composition and its distribution. Furthermore, the 3MP comonomer unit was found to be included in the crystalline phase in some fractions.

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The effect of graphite oxide (GO) as the enforcing filler on the properties of poly(ϵ-caprolactone) (PCL) was investigated in this study. Through the introduction of GO, the Young's modulus of PCL was increased from 340 to 1000 MPa, and the tensile strength of PCL was increased from 15 to 26 MPa. Furthermore, the interlayer distance of GO (0.6 nm) was found to expand to 1.1 nm in the PCL/GO composite, which indicated the intercalation of the PCL chain into the GO layers. Because of this intercalation structure of the PCL/GO composite, GO showed a higher reinforcing effect than graphite on the mechanical properties of PCL. The intercalation should have enabled much effective load transfer in the PCL/GO composites. Moreover, GO showed a nucleating effect toward the crystallization of PCL, as the nonisothermal crystallization peak temperature shifted from 25°C for pure PCL to about 34°C for the PCL/GO composites. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2008

Joint Research on Environmental Science and Technology for the Earth

In this study, fog simulations were conducted using the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) in and around the Yodo River Basin, Japan. The purpose is to investigate the MM5 performance of fog simulation for long-term periods. The simulations were performed for January, February, March, and July, 2005 with a coarse 3-km and a nested fine 1-km grid domains. Results of the simulations were compared with data from ten meteorological observatories, fog sampling site in Mt. Rokko, and visibility measurement sites along the Second Meishin Expressway. At the meteorological observatories, the MM5 predictions agreed well with the observed temperature and specific humidity. In the Mt. Rokko region, MM5 generally reproduced the occurrence of relatively thick fog events but tended to overestimate liquid water content (LWC) of fog (by factors of 2.2-3.3 in terms of monthly mean LWC). In the Second Meishin Expressway region, while MM5 identified the specific sites at which fog either frequently or seldom occurs, the model underestimated the monthly fog frequencies by factors of more than 1.5. Overall, MM5 reproduced the general trend of fog events, and the model performance may be improved by using more adequate land surface data and suitable physics options for our study.