Exploring the potential of canopy reflectance spectra for estimating organic carbon content of aboveground vegetation in coastal wetlands

ABSTRACT

Accurate estimation of organic carbon content or carbon storage of coastal wetland vegetation is essential for understanding the carbon cycle of coastal wetland ecosystems. This study aimed to explore the potential of canopy spectra of coastal wetland vegetation in estimating the organic carbon content of aboveground vegetation (OCCAV). A total of 54 representative vegetation quadrats were selected from coastal wetlands in Jiangsu Province, China, and their canopy reflectance spectra were measured using an in situ field spectrometer. In particular, two advanced spectral algorithms, fractional-order derivative (FOD) and optimal band combination algorithm, were used for spectral pre-treatments. Partial least squares regression (PLSR) and support vector machine (SVM) were employed to establish the OCCAV estimation models. Accuracies of the models constructed by the processed spectral parameters were compared with that of the models corresponding to five traditional pre-treatment methods and 15 common vegetation indices. Results showed that the FOD method captured subtler spectral characteristics than the first and second derivatives. The optimal estimation accuracies of PLSR and SVM models were obtained based on 0.75 and 1.50 order derivative spectra, respectively, and the ratios of performance to interquartile range (RPIQ) of the models were 2.57 and 2.97, respectively. Moreover, the optimal band combination algorithm effectively extracted the sensitive spectral parameters related to OCCAV, and the accuracy of the model established based on the spectral parameters extracted by this algorithm was generally better than that of the model obtained by the common vegetation indices. The optimal estimation result was achieved by the SVM model based on the optimized ratio vegetation index, with an RPIQ of 3.10. In summary, this research provides a theoretical basis for future studies on estimating the organic carbon and carbon storage of wetland vegetation based on large-scale canopy hyperspectral images and also helps to improve the knowledge of the carbon cycle of wetland ecosystems.

Acknowledgements

The authors acknowledge financial support from the National Natural Science Foundation of China (No. 41871203).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China [No. 41871203].