Tracing Leaf Photosynthetic Parameters Using Hyperspectral Indices in an Alpine Deciduous Forest

Jia Jin,Quan Wang,Bayu Arief Pratama

doi:10.3390/rs12071124

Abstract

Leaf photosynthetic parameters are important in understanding the role of photosynthesis in the carbon cycle. Conventional approaches to obtain information on the parameters usually involve long-term field work, even for one leaf sample, and are, thus, only applicable to a small area. The utilization of hyperspectral remote sensing especially of various vegetation indices is a promising approach that has been attracting increasing attention recently. However, most hyperspectral indices are only applicable to a specific area and specific forest stands, depending heavily on the conditions from which the indices are developed. In this study, we tried to develop new hyperspectral indices for tracing the two critical photosynthetic parameters (the maximum rate of carboxylation, Vcmax and the maximum rate of electron transport, Jmax) that are at least generally applicable for alpine deciduous forests, based on original hyperspectral reflectance, first-order derivatives, and apparent absorption spectra. In total, ten types of hyperspectral indices were screened to identify the best indices, and their robustness was determined using the ratio of performance to deviation (RPD) and Akaike’s Information Criterion corrected (AICc). The result revealed that the double differences (DDn) type of indices using the short-wave infrared (SWIR) region based on the first-order derivatives spectra performed best among all indices. The specific DDn type of indices obtained the RPD values of 1.43 (R2 = 0.51) for Vcmax and 1.68 (R2 = 0.64) for Jmax, respectively. These indices have also been tested using the downscaled dataset to examine the possibilities of using hyperspectral data derived from satellite-based information. These findings highlight the possibilities of tracing photosynthetic capacity using hyperspectral indices.

Highlights

Global climate change has been projected to reduce global net primary production (NPP) and carbon stocks from soil [1], and has become a global threat to humans
Such an effort could be more effective if supported by increased reforestation, as tree species and all chlorophyll plants are important for mitigation purposes, since leaves capture CO2 from the atmosphere, and combine it with water and energy from the sun to produce carbohydrates, a well-known process called photosynthesis that is critical for the carbon cycle [3,4]
New hyperspectral vegetation indices have been developed for tracing photosynthetic parameters in an alpine deciduous forest containing six species

Summary

Introduction

Global climate change has been projected to reduce global net primary production (NPP) and carbon stocks from soil [1], and has become a global threat to humans. Tremendous efforts have been made to reduce the impact of global climate change, for which the control of greenhouse gases (GHGs) emissions has become a popular solution [2]. Such an effort could be more effective if supported by increased reforestation, as tree species and all chlorophyll plants are important for mitigation purposes, since leaves capture CO2 from the atmosphere, and combine it with water and energy from the sun to produce carbohydrates, a well-known process called photosynthesis that is critical for the carbon cycle [3,4]. The maximum rate of rubisco carboxylation (Vcmax), and the maximum rate of photosynthesis electron transport (Jmax), are essential to the model, and are, important to understand the exchange of carbon between the atmosphere and the terrestrial ecosystem [7]

Methods

Results

Discussion

Conclusion