Minimum variance based-Bayes Combination for prediction of soil properties on Vis-NIR reflectance spectroscopy

Abstract. Soil organic carbon (SOC) is a crucial indicator of soil fertility, maintaining soil health and sustaining the productivity of agro-ecosystem. Rapid, reliable and cost effective assessment of soil properties specially for SOC is important for monitoring soil fertility status along with soil health. Conventional chemical analysis of any soil property is hazardous, tedious and time consuming. So, the visible near infrared (VIS-NIR) reflectance spectroscopy can provide an effective alternative technique for rapid and ecofriendly measurement of soil properties. In view of this, a key soil fertility parameter SOC was examined through diffuse reflectance spectroscopy. Georeferenced surface soil samples (0&ndash;15&thinsp;cm) were collected from a rice-wheat field of the study area for both chemical and spectral analysis. A viable statistical technique namely partial least square regression (PLSR) technique were used to correlate the measured properties with soil reflectance spectra and for developing spectral model. The predictive performance of newly developed spectral model was evaluated through different reliable indices like root mean square of error of prediction (RMSEP), coefficient of determination (R2) and ratio of performance deviation (RPD). The result showed that the R2 value for SOC is 0.44, RMSEP is 0.07 and the RPD value is 1.57 in the validation dataset. The RPD value indicating that SOC can be reliably predicted using the hyperspectral model or reflectance analysis. So, this hyperspectral modeling technique can be successfully employed for monitoring soil health as well as for sustainable agriculture.

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Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties

Scientific Research Journal ◽

10.24191/srj.v11i1.5416 ◽

2014 ◽

Vol 11 (1) ◽

pp. 15

Author(s):

Set Foong Ng ◽

Pei Eng Ch’ng ◽

Yee Ming Chew ◽

Kok Shien Ng

Keyword(s):

Soil Properties ◽

Lagrange Multipliers ◽

Unbiased Estimator ◽

Soil Property ◽

Minimum Variance ◽

Distance Method ◽

True Value ◽

A Site ◽

Method Of Lagrange Multipliers ◽

Inverse Distance

Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.

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Optimized Method for Mapping Inorganic Pigments by Means of Multispectral Imaging Combined with Hyperspectral Spectroscopy for the Study of Vincenzo Pasqualoni’s Wall Painting at the Basilica of S. Nicola in Carcere in Rome

Minerals ◽

10.3390/min11080839 ◽

2021 ◽

Vol 11 (8) ◽

pp. 839

Author(s):

Lucilla Pronti ◽

Giuseppe Capobianco ◽

Margherita Vendittelli ◽

Anna Candida Felici ◽

Silvia Serranti ◽

...

Keyword(s):

Multispectral Imaging ◽

Imaging System ◽

Principal Component ◽

Reflectance Spectroscopy ◽

Wall Painting ◽

High Spectral Resolution ◽

Spectral Difference ◽

Color Palette ◽

A Minor ◽

Nir Reflectance Spectroscopy

Multispectral imaging is a preliminary screening technique for the study of paintings. Although it permits the identification of several mineral pigments by their spectral behavior, it is considered less performing concerning hyperspectral imaging, since a limited number of wavelengths are selected. In this work, we propose an optimized method to map the distribution of the mineral pigments used by Vincenzo Pasqualoni for his wall painting placed at the Basilica of S. Nicola in Carcere in Rome, combining UV/VIS/NIR reflectance spectroscopy and multispectral imaging. The first method (UV/VIS/NIR reflectance spectroscopy) allowed us to characterize pigment layers with a high spectral resolution; the second method (UV/VIS/NIR multispectral imaging) permitted the evaluation of the pigment distribution by utilizing a restricted number of wavelengths. Combining the results obtained from both devices was possible to obtain a distribution map of a pictorial layer with a high accuracy level of pigment recognition. The method involved the joint use of point-by-point hyperspectral spectroscopy and Principal Component Analysis (PCA) to identify the pigments in the color palette and evaluate the possibility to discriminate all the pigments recognized, using a minor number of wavelengths acquired through the multispectral imaging system. Finally, the distribution and the spectral difference of the different pigments recognized in the multispectral images, (in this case: red ochre, yellow ochre, orpiment, cobalt blue-based pigments, ultramarine and chrome green) were shown through PCA false-color images.

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