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Retrieving nitrogen isotopic signatures from fresh leaf reflectance spectra: disentangling ?(15)N from biochemical and structural leaf properties.

ABSTRACT: Linking remote sensing methodology to stable isotope ecology provides a promising approach to study ecological processes from small to large spatial scales. Here, we show that ?(15)N can be detected in fresh leaf reflectance spectra of field samples along a spatial gradient of increasing nitrogen input from an N2-fixing invasive species. However, in field data it is unclear whether ?(15)N directly influences leaf reflectance spectra or if the relationship is based on covariation between ?(15)N and foliar nitrogen content or other leaf properties. Using a (15)N-labeling approach, we experimentally varied ?(15)N independently of any other leaf properties in three plant species across different leaf developmental and physiological states. ?(15)N could successfully be modeled by means of partial least squares (PLSs) regressions, using leaf reflectance spectra as predictor variables. PLS models explained 53-73% of the variation in ?(15)N within species. Several wavelength regions important for predicting ?(15)N were consistent across species and could furthermore be related to known absorption features of N-containing molecular bonds. By eliminating covariation with other leaf properties as an explanation for the relationship between reflectance and ?(15)N, our results demonstrate that (15)N itself has an inherent effect on leaf reflectance spectra. Thus, our study substantiates the use of spectroscopic measurements to retrieve isotopic signatures for ecological studies and encourages future development. Furthermore, our results highlight the great potential of optical measurements for up-scaling isotope ecology to larger spatial scales.

SUBMITTER: Hellmann C

PROVIDER: S-EPMC4416452 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Project description:Heavy metals contaminations in mining areas aroused wide concerns globally. Efficient evaluation of its pollution status is a basis for further soil reclamation. Visible and near-infrared reflectance (Vis-NIR) spectroscopy has been diffusely used for retrieving heavy metals concentrations. However, the reliability and feasibility of calibrated models were still doubtful. The present study estimated zinc (Zn) concentrations via the random forest (RF) and partial least squares regression (PLSR) using ground in-situ Zn concentrations as well as soil spectral reflectance at an Opencast Coal Mine of Ordos, China in February 2020. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and the ratio of performance to deviation (RPD) were selected to assess the robustness of the methods in estimating Zn contents. Moreover, the characteristic bands were chosen by Pearson correlation analysis and Boruta Algorithm. Finally, the comparison between RF and PLSR combined with eight spectral reflectance transformation methods was conducted for four concentration groups to determine the optimal model. The results indicated that: (1) Zn contents represented a skewed distribution (coefficient of variation (CV) = 33%); (2) the spectral reflectance tended to decrease with the increase of Zn contents during 580-1850 nm based on Savitzky-Golay smoothing (SG); (3) the continuous wavelet transform (CWT) demonstrated higher effectiveness than other spectral reflectance transformation methods in enhancing spectral responses, the R2 between Zn contents and the soil spectral reflectance achieved the highest (R2 = 0.71) by using CWT; (4) the RF combined with CWT exhibited the best performance than other methods in the current study (R2 = 0.97, RPD = 3.39, RMSE = 1.05 mg kg-1, MAE = 0.79 mg kg-1). The current study supplied a scientific scheme and theoretical support for predicting heavy metals concentrations via the Vis-NIR spectral method in possible contaminated areas such as coal mines and metallic mineral deposit areas.

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Retrieving nitrogen isotopic signatures from fresh leaf reflectance spectra: disentangling ?(15)N from biochemical and structural leaf properties.

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