First Derivative Reflectance Spectra Research Articles

Estimating leaf nitrogen concentration considering unsynchronized maize growth stages with canopy hyperspectral technique

Abstract Accurate monitoring of the leaf nitrogen concentration (LNC) in maize can provide a fundamental basis for effective N management. In recent decades, many spectral indices and algorithms can accurately estimate the crop N status during different growth stages of maize. However, the effect of unsynchronized maize growth stages on these spectral models is rarely considered. The objectives of this study were to verify the predictive ability of the published vegetation indices (VIs), the partial least squares (PLS) regression and the two-band optimal combinations algorithms, and to determine the most accurate method for assessing the LNC of unsynchronized growth stages in maize. Canopy raw and first-derivative reflectance (FDR) spectra, and destructive measurements of the maize LNC were collected in 2016–2018 in different ecological areas, unsynchronized growth stages, cultivars, plant densities, and N rates. The published VIs and new 2-band VIs and their band combinations varied across different growth stages and were not affected by unsynchronized growth stages. The red-edge chlorophyll index (CIred edge), which was identified across growth stages, performed quite well for LNC estimation, but performed unsatisfactorily when compared to the new 2-band VIs that were developed in this study. The best spectral index of the green or red and red-edge or near-infrared band combination based on FDR spectra had a good diagnostic effect for LNC of maize across the four growth stages, in which the novel normalized difference spectral indices (NDSI) effect was optimal in the V9 (9-leaf stage), VT (tasseling stage) and R1 (silking stage) stage and the novel ratio spectral indices (RSI) was the best in the R3 (milk stage) stage. Compared to PLS regression based on raw full-range hyperspectral data, the PLS regression for estimating LNC across four growth stages based on FDR full-range hyperspectral data showed a higher accuracy, with an average coefficient of determination (rval2) of 0.87 and average root mean square error (RMSEval) of 0.18. In addition, the average rval2 for the PLS regression based on selected FDR wavelengths increased by 2.40% and the average RMSEval decreased by 14.8%, compared with the best performing VIs during the four growth stages. It is concluded that the best 2-band VIs and the PLS regression based on selected FDR wavelengths provide a useful explorative tool for estimating LNC of maize across years, ecological areas, and unsynchronized growth stages.

Spectral Deconvolution for Dimension Reduction and Differentiation of Seagrasses: Case Study of Gulf St. Vincent, South Australia

Seagrasses are a vulnerable and declining coastal habitat, which provide shelter and substrate for aquatic microbiota, invertebrates, and fishes. More accurate mapping of seagrasses is imperative for their sustainability but is hindered by the lack of data on reflectance spectra representing the optical signatures of individual species. Objectives of this study are: (1) To determine distinct characteristics of spectral profiles for sand versus three temperate seagrasses (Posidonia, Amphibolis, and Heterozostera); (2) to evaluate the most efficient derivative analysis method of spectral reflectance profiles for determining benthic types; and to assess the influences of (3) site location and (4) the water column on spectral responses. Results show that 566:689 and 566:600 bandwidth ratios are useful in separating seagrasses from sand and from detritus and algae, respectively; first-derivative reflectance spectra generally is the most efficient method, especially with deconvolution analyses further helping to reveal and isolate 11 key wavelength dimensions; and differences between sites and water column composition, which can include suspended particulate matter, both have no effect on endmembers. These findings helped develop a spectral reflectance library that can be used as an endmember reference for remote sensing, thereby providing continued monitoring, assessment, and management of seagrasses.

Estimation of semiconductor-like pigment concentrations in paint mixtures and their differentiation from paint layers using first-derivative reflectance spectra

Identification of the techniques employed by artists, e.g. mixing and layering of paints, if used together with information about their colour palette and style, can help to attribute works of art with more confidence. In this study, we show how the pigment composition in binary paint mixtures can be quantified using optical-reflectance spectroscopy, by analysis of the peak features corresponding to colour-transition edges in the first-derivative spectra. This technique is found to be more robust than a number of other spectral-analysis methods, which can suffer due to shifts in the transition edges in mixed paints compared to those observed in spectra of pure ones. Our method also provides a means of distinguishing paint mixtures from layering in some cases. The spectroscopy also shows the presence of multiple electronic transitions, accessible within a narrow energy range, to be a common feature of many coloured pigments, which electronic-structure calculations attribute to shallow band edges. We also demonstrate the successful application of the reflectance-analysis technique to painted areas on a selection of medieval illuminated manuscripts.

Comparison of Spectral Statistical Parameters about Plant Biochemical Concentration Determination with Remote Sensing

Based on analyzing the concentration of two biochemical components, including total chlorophyll and nitrogen, we establish the statistical relationships between the concentration and inverse\|log spectra(log 1/R)through the stepwise multiple regression method. So do the relationships between the concentration and several transformations of reflectance such as standard first derivative reflectance spectra (FDS), absorption band depths following continuum removal and normalization against band depth at the center of the absorption feature (BNC), absorption band depths following continuum removal and normalization against the area of the absorption feature(BNA).The results show very different performances among these four spectral statistical parameters ,including the selections of bands, the results of stepwise multiple regression and the effects of the statistical equations. This just illustrates uncertainty when using statistical methods to determine plant biochemical concentration. However, we still get good prediction performance for chlorophyll and nitrogen with BNC and BNA. Especially, R value of the correlation between estimated and observed nitrogen concentration is 0.960. We get these results on a spectra/biochemical data set from dried and ground Douglas Fir. Further research work should be done to determine foliar biochemical concentration from field, airborne and spaceborne spectra.

Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies

In an effort to further develop the methods needed to remotely sense the biochemical concentration of plant canopies, we report the results of an experiment to estimate the concentration of 12 foliar biochemicals (chlorophyll a, chlorophyll b, total chlorophyll, lignin, nitrogen, cellulose, water, phosphorous, protein, amino acids, sugar, starch) from reflectance spectra of dried and ground slash pine needles. The three methodologies employed used stepwise regression and either of the following: (i) standard first derivative reflectance spectra (FDS), (ii) absorption band depths, following continuum removal and normalisation against band depth at the centre of the absorption feature (BNC) or (iii) absorption band depths, following continuum removal and normalisation against the area of the absorption feature (BNA). These latter two methodologies have been proposed in this journal [Remote Sens. Environ., 67 (1999) 267.] on the basis of an experiment using reflectance spectra of dried and ground tree leaves and the concentration of three foliar biochemicals: nitrogen, lignin and cellulose. All three methodologies were implemented on a spectra/biochemical data set from early in the growing season and tested on a similar data set from late in the growing season. The accuracy with which foliar biochemical concentration could be estimated, while high for all methodologies, was highest when using the two proposed by Kokaly and Clark. At an illustrative R 2 threshold of .85 (between estimated and observed biochemical concentration), all three methodologies could be used to estimate total chlorophyll, nitrogen, cellulose and sugar; in addition, the BNC methodology could be used to estimate chlorophyll a and b, and in addition to this, the BNA methodology could be used to estimate lignin and water. Given the advantages offered by the Kokaly and Clark methodologies (over and above the standard methodology) for a wide range of foliar biochemicals, it is recommended that their utility is investigated for the estimation of foliar biochemical concentration from field, airborne and spaceborne spectra.