Influence of polarized reflection on airborne remote sensing of canopy foliar nitrogen content

ABSTRACT

Radiation detected by sensors can reveal information regarding canopy structure and leaf surface properties. It has been shown that the scattering coefficient (W_λ), a ‘pure’ spectrum obtained after correction for the effect of canopy structure on the bidirectional reflectance factor (BRF), can provide information about biochemical composition; e.g. nitrogen content, more objectively. However, specular radiation reflected from leaf surface (without any information from the leaf interior) has a potential influence on W_λ and thus consequently affects the estimation of canopy nitrogen content (CNC). The so-called specular reflection influence has rarely been studied. Polarized reflectance (R_p), as part of the specular reflectance, was used to assess this kind of influence. To model the accurate R_p within the study area, six bidirectional polarization distribution function (BPDF) models were intercompared, using the existing POLDER/PARASOL R_p database, to produce R_p for three vegetation types: needleleaf, broadleaf, and mixed forest. Then, correction for polarized reflection and canopy structure was made to yield a more reasonable and accurate W_λ. An efficient interval partial least-squares regression (iPLSR) method for CNC estimation was used to analyse the impact of polarized reflection on both the 400 nm–2,500-nm canopy W_λ and CNC estimation. The results showed that the contribution of polarization was greater in the strong-absorption spectral region, with an influence on W_λ estimation of up to 25% and 28% in the visible and short-wave infrared regions, respectively. Also, improvements of 0.93% in average regarding the relative mean square error of cross-validation (RMSECV) were seen in CNC estimation accuracy. Moreover, sensitivity analyses of BPDF model parameters, and of the fixed total number of intervals in the iPLSR were also conducted. Improvements with an average of 1.19% regarding the RMSECV was consistently seen in the accuracy of CNC regression after polarization correction, no matter how many fixed intervals were. The results also gave guides on the selection of spectral regions used to estimate forest CNC.

Acknowledgements

This work was supported by the National Key R&D Program of China (Grant No. 2017YFB0503004); and National Natural Science Foundation of China (Grant No. 41842048). We thank Dr. Zhihui Wang (Department of Forest and Wildlife Ecology, University of Wisconsin-Madison) for generously providing the plot level CNC data and spectrum, as well as for her kind support.

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No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

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Funding

This work was supported by the National Natural Science Foundation of China [41842048]; National Key R&D Program of China [2017YFB0503004].