Advances in hyperspectral remote sensing of vegetation traits and functions

The functions and traits of plants are key to understanding and predicting the adaptation of ecosystems to environmental changes. Remote sensing has been used to monitor the status of vegetation across multiple spatial and temporal scales. The remote sensing of vegetation is now undergoing a paradigm shift from monitoring structural parameters to monitoring functional traits. In particular, recent advances in hyperspectral techniques of remote sensing provide an opportunity to map vegetation traits and functions over a range of scales. In this editorial, we first present the background of the recent advances in the remote sensing of vegetation traits and functions and solar-induced fluorescence (SIF) of chlorophyll. We then summarize eight of the papers in this special issue that focus on new remote-sensing techniques and algorithms developed for retrieving plant functional traits, such as pigment and nitrogen contents and functional parameters. These contributions cover two major scientific themes: (1) estimating and monitoring plant traits and functions and (2) interpreting and understanding remotely sensed SIF signals. The research in this special issue will improve the development of the satellite remote sensing of plant traits and functions, allowing for improved estimation of vegetation processes such as photosynthesis and its associated water and carbon cycles.

Automated summary

This article introduces the recent advances in remote sensing of plant traits and functions, including the shift from monitoring structural parameters to functional traits and the use of hyperspectral techniques to monitor vegetation status across various scales. The eight papers mentioned in this editorial focus on developing new remote sensing techniques and algorithms for retrieving plant functional traits, which will help improve the estimation of vegetation processes such as photosynthesis, water cycle, and carbon cycle.