Universita Della Tuscia

Viterbo, Italy

Universita Della Tuscia

Viterbo, Italy
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Mu X.,Beijing Normal University | Mu X.,CSIRO | Hu R.,Beijing Normal University | Zeng Y.,CAS Institute of Remote Sensing | And 10 more authors.
Agricultural and Forest Meteorology | Year: 2017

Accurate and efficient in situ measurement methods of leaf area index (LAI) and leaf angle distribution (LAD) are needed to estimate the fluxes of water and energy in agricultural settings. However, available methods: to estimate these two parameters, especially LAD, are limited. In this study, we propose a field measurement method using multi-angular digital images to estimate LAI and LAD simultaneously from the area proportions of: (i) sunlit soil; (ii) sunlit leaves; (iii) shaded soil; and (iv) shaded leaves. A new expression of the fraction of sunlit leaves is developed based on the radiative transfer theory. Coupling the measured and modeled fractions with an optimization scheme, LAI and the LAD parameters are derived from inverting a fractional model of sunlit and shaded leaves and soil. Through four tests using simulated scenes and in situ measurements for row crops, it is determined that our method performs well. The absolute error of LAI estimation is less than 0.1 when LAI is low (i.e., <1.2), and the absolute deviations of LAI estimates are approximately 0.5 when the reference LAI is 3.5. The estimation errors of LAI and the G function (a representative of LAD which quantifies the projection of unit foliage area) for in situ measurements are respectively less than 0.2 and 0.06 in general. In addition, the accuracy of estimation is even higher when leaves are simulated as randomly distributed disks or observations from multiple azimuth planes are used. One of the most interesting features of this method is its ability to estimate reasonable LAD directly from the fractions of sunlit and shaded leaves, even when LAI is high (i.e., >3), so little background soil is seen. The sensitivity and uncertainty analysis is consistent with the estimation errors. Theoretically, the application of this method is not limited to row crops or to field measurement, as the derived formulae of sunlit and shaded components can be used for other types of vegetation by introducing the clumping index and can be used in the modeling of canopy vegetation parameters (e.g., canopy reflectance). © 2017


Zhou X.,CAS Institute of Remote Sensing | Zhou X.,University of Chinese Academy of Sciences | Huang W.,CAS Institute of Remote Sensing | Kong W.,CAS Institute of Remote Sensing | And 3 more authors.
International Journal of Applied Earth Observation and Geoinformation | Year: 2017

Leaf carotenoids content (LCar) is an important indicator of plant physiological status. Accurate estimation of LCar provides valuable insight into early detection of stress in vegetation. With spectroscopy techniques, a semi-empirical approach based on spectral indices was extensively used for carotenoids content estimation. However, established spectral indices for carotenoids that generally rely on limited measured data, might lack predictive accuracy for carotenoids estimation in various species and at different growth stages. In this study, we propose a new carotenoid index (CARI) for LCar assessment based on a large synthetic dataset simulated from the leaf radiative transfer model PROSPECT-5, and evaluate its capability with both simulated data from PROSPECT-5 and 4SAIL and extensive experimental datasets: the ANGERS dataset and experimental data acquired in field experiments in China in 2004. Results show that CARI was the index most linearly correlated with carotenoids content at the leaf level using a synthetic dataset (R2 = 0.943, RMSE = 1.196 μg/cm2), compared with published spectral indices. Cross-validation results with CARI using ANGERS data achieved quite an accurate estimation (R2 = 0.545, RMSE = 3.413 μg/cm2), though the RBRI performed as the best index (R2 = 0.727, RMSE = 2.640 μg/cm2). CARI also showed good accuracy (R2 = 0.639, RMSE = 1.520 μg/cm2) for LCar assessment with leaf level field survey data, though PRI performed better (R2 = 0.710, RMSE = 1.369 μg/cm2). Whereas RBRI, PRI and other assessed spectral indices showed a good performance for a given dataset, overall their estimation accuracy was not consistent across all datasets used in this study. Conversely CARI was more robust showing good results in all datasets. Further assessment of LCar with simulated and measured canopy reflectance data indicated that CARI might not be very sensitive to LCar changes at low leaf area index (LAI) value, and in these conditions soil moisture influenced the LCar retrieval accuracy. © 2016


Cacchiarelli L.,Universita Della Tuscia | Carbone A.,Universita Della Tuscia | Laureti T.,Universita Della Tuscia | Sorrentino A.,Universita Della Tuscia
Journal of Wine Research | Year: 2014

The purpose of the paper is to contribute to understanding the role and effectiveness of different quality clues in the creation of value for the main wines of the Lazio region. The study presents a hedonic price model. An ordinary least squares and a quantile regression models were estimated. The latter is able to detect additional patterns related to the effects of the covariates. Prices are regressed on wine color, sub-regional area of origin, the type of certification of origin, and on experts' evaluation. The analysis is based on data released by three major Italian wine guides: Gambero Rosso, l'Espresso, and AIS (Italian Sommelier Association). Results show that: (i) white and red wines follow two different price patterns; (ii) prices are correlated with experts' evaluation; (iii) the impact of the latter is higher when other quality clues, such as geographical indications, are less effective; (iv) the role of different quality clues varies at different price levels and it is different for red and white wines; overall, wines from the Lazio region are associated with poor to mediocre quality levels. This may explain the decline in reputation and in market share that these wines are experiencing after centuries of popularity. © 2014, © 2014 Taylor & Francis.


Force fluctuations recorded in an atomic force spectroscopy experiment, during the approach of a tip functionalized with biotin towards a substrate charged with avidin, have been analyzed by a wavelet transform. The observation of strong transient changes only when a specific biorecognition process between the partners takes place suggests a drastic modulation of the force fluctuations when biomolecules recognize each other. Such an analysis allows to investigate the peculiar features of a biorecognition process. These results are discussed in connection with the possible role of energy minima explored by biomolecules during the biorecognition process. © 2015, Springer Science+Business Media Dordrecht.

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