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Vibrationally resolved electronic absorption spectra including the effect of vibrational pre-excitation are computed in order to interpret and predict vibronic transitions that are probed in the Vibrationally Promoted Electronic Resonance (VIPER) experiment [L. J. G. W. van Wilderen et al., Angew. Chem., Int. Ed. 53, 2667 (2014)]. To this end, we employ time-independent and time-dependent methods based on the evaluation of Franck-Condon overlap integrals and Fourier transformation of time-domain wavepacket autocorrelation functions, respectively. The time-independent approach uses a generalized version of the FCclasses method [F. Santoro et al., J. Chem. Phys. 126, 084509 (2007)]. In the time-dependent approach, autocorrelation functions are obtained by wavepacket propagation and by the evaluation of analytic expressions, within the harmonic approximation including Duschinsky rotation effects. For several medium-sized polyatomic systems, it is shown that selective pre-excitation of particular vibrational modes leads to a redshift of the low-frequency edge of the electronic absorption spectrum, which is a prerequisite for the VIPER experiment. This effect is typically most pronounced upon excitation of modes that are significantly displaced during the electronic transition, such as ring distortion modes within an aromatic π-system. Theoretical predictions as to which modes show the strongest VIPER effect are found to be in excellent agreement with experiment.

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Abstract:The monitoring of ecosystems alterations has become a crucial task in order to develop valuable habitats for rare and threatened species. The information extracted from hyperspectral remote sensing data enables the generation of highly spatially resolved analyses of such species' habitats. In our study we combine information from a species ordination with hyperspectral reflectance signatures to predict occurrence probabilities for Natura 2000 habitat types and their conservation status. We examine how accurate habitat types and habitat threat, expressed by pressure indicators, can be described in an ordination space using spatial correlation functions from the geostatistic approach. We modeled habitat quality assessment parameters using floristic gradients derived by non-metric multidimensional scaling on the basis of 58 field plots. In the resulting ordination space, the variance structure of habitat types and pressure indicators could be explained by 69% up to 95% with fitted variogram models with a correlation to terrestrial mapping of >0.8. OPEN ACCESSRemote Sens. 2015, 7 2872Models could be used to predict habitat type probability, habitat transition, and pressure indicators continuously over the whole ordination space. Finally, partial least squares regression (PLSR) was used to relate spectral information from AISA DUAL imagery to floristic pattern and related habitat quality. In general, spectral transferability is supported by strong correlation to ordination axes scores (R 2 = 0.79-0.85), whereas second axis of dry heaths (R 2 = 0.13) and first axis for pioneer grasslands (R 2 = 0.49) are more difficult to describe.

The estimation of the soil organic carbon (SOC) content plays an important role for carbon sequestration in the context of climate change, food security and soil degradation. Reflectance spectroscopy has proven to be a promising technique for SOC quantification in the laboratory and increasingly from air-and spaceborne platforms, where hyperspectral imagery provides great potential for mapping SOC on larger scales with regular updates. When applied on larger scales, soil prediction accuracy decreases due to the inhomogeneity of samples. In this paper, we examined if spectral clustering of the LUCAS EU-wide topsoil database is successful without using other covariates than the spectral database and can improve SOC model performance compared to a reference model that was calibrated on the whole database without clustering. Different clustering methodologies were tested, including a k-means clustering based on principal component analyses or based on spectral feature variables, combined with partial least squares regression (PLSR) models, and a clustering based on a local PLSR approach which builds a different multivariate model for each sample to be predicted. Furthermore, in order to allow for subsequent application to hyperspectral remote sensing data, atmospheric water wavelengths were removed from the analyses. The local PLSR approach achieved best results and was additionally applied to LUCAS spectra resampled to the upcoming hyperspectral EnMAP sensor which led to good results: R 2 = 0.66, RMSEP = 5.78 g kg -1 and RPIQ = 1.93. The k-means clustering approach showed slightly better results than the reference model. Overall, our results showed similar performances for SOC prediction models compared to other approaches using PLSR with a larger spectral range and other soil parameters as covariates. This study shows that (i) it is possible to transfer the local PLSR approach onto a wavelengths reduced spectral library and to predict estimations of SOC at low-cost with reasonable accuracy based on large scale soil databases; and (ii) that the local regression approach is a valuable tool for SOC prediction models based solely on spectral data without the use of other soil covariates.