Estimating Pasture Quality of Fresh Vegetation Based on Spectral Slope of Mixed Data of Dry and Fresh Vegetation—Method Development

Abstract

Abstract:The main objective of the present study was to apply a slope-based spectral method to both dry and fresh pasture vegetation. Differences in eight spectral ranges were identified across the near infrared-shortwave infrared (NIR-SWIR) that were indicative of changes in chemical properties. Slopes across these ranges were calculated and a partial least squares (PLS) analytical model was constructed for the slopes vs. crude protein (CP) and neutral detergent fiber (NDF) contents. Different datasets with different numbers of fresh/dry samples were constructed to predict CP and NDF contents. When using a mixed-sample dataset with dry-to-fresh ratios of 85%:15% and 75%:25%, the correlations of CP (R 2 = 0.95, in both) and NDF (R 2 = 0.84 and 0.82, respectively) were almost as high as when using only dry samples (0.97 and 0.85, respectively). Furthermore, satisfactory correlations were obtained with a dry-to-fresh ratio of 50%:50% for CP (R 2 = 0.92). The OPEN ACCESSRemote Sens. 2015, 7 8046 results of our study are especially encouraging because CP and NDF contents could be predicted even though some of the selected spectral regions were directly affected by atmospheric water vapor or water in the plants.

Studying Vegetation Salinity: From the Field View to a Satellite-Based Perspective

“…Begley et al [20] detected differences in the absorption bands of water at 1806 nm that were well correlated with different concentrations of NaCl in meat. A similar relationship between the total area of water absorption and crude protein content was found by Lugassi et al [35].…”

“…For example, to obtain 50% accuracy, we need a sample size of 103 spectral measurements, whereas to obtain 90% accuracy, 286 measurements are needed. Taking into account all of the above, we can argue that in order to achieve a higher accuracy of salinity estimation, future studies should be expanded and include the following steps: (1) using the dataset with the much wider range of chemical reference values and vegetation types; (2) larger calibration and validation dataset samples; and (3) incorporating multiple slopes into the analyses (similarly to Lugassi et al [35]). …”

“…To develop a model to assess Cl and Na content in vegetation based on the slope method [34,35], we used the common CR spectral technique [9,[36][37][38][39][40]. CR normalizes reflectance spectra to a common baseline, enabling the identification of individual absorption features.…”

“…The spectral absorption features centered at 1940 nm have been found to be good indicators of plant water content [34,45,46]. Therefore, the area between the straight line and the absorption line ( Figure 2) was calculated (see [35] for more details). …”

Carbohydrate-based phenotyping of the green macroalga Ulva fasciata using near-infrared spectrometry: potential implications for marine biorefinery

“…In addition, the same spectra and similar modelling approaches could be used in further studies for quantification of other important biorefinery constituents of Ulva biomass, such as starch, proteins, pigments and special chemicals. For example, Lugassi et al (2015b) concluded that crude protein and neutral detergent fibre contents could be predicted using the spectral slope method for dry and wet vegetation.…”