Global Foliar Traits

Project title: Foliar Functional Traits from EMIT

EMIT is pioneering a new generation of imaging spectrometers, enabling detailed Earth and ecosystem characterization. It provides a unique opportunity to prototype data products for the upcoming Surface Biology and Geology (SBG) mission. Our focus is mapping foliar functional traits from space, a priority identified by the 2017-2027 Decadal Survey. Unlike past efforts relying on airborne imaging or static upscaling of in-situ data, EMIT’s broad coverage allows for large-scale trait characterization, especially in data-scarce regions. This will provide us with information about vegetation characteristics at an unprecedented scale. We are developing models to map foliar traits by leveraging existing datasets from California, NEON, South Africa, India, and Central Asia (notably Mongolia), while also integrating in-situ data from sources like TRY and sPlotOpen for global trait upscaling. Our primary focus is on dry, semi-dry, and seasonally dry ecosystems, key areas imaged by EMIT, but we will extend this effort to the full extent of EMIT acquisitions, which ultimately will provide data for locations such as the tropics for which little imaging spectroscopy data exists. We are mapping traits like nitrogen and leaf mass per area (LMA/SLA) and exploring additional traits (e.g., phenolics, carbohydrates, lignin, cellulose) where sufficient in-situ data exist. A crucial aspect is incorporating vegetation fractional cover, which varies significantly in drylands. This research will inform models for future spaceborne sensors like the SBG imaging spectrometer. Finally, we are investigating trait trade-offs and their relationships with environmental factors such as temperature, soil, and rainfall seasonality. This study fills critical knowledge gaps in the global variation in plant functional traits, particularly in drylands, which remain underexplored compared to temperate forests.

Project Team

	Philip Townsend
	University of Wisconsin-Madison	ptownsend@wisc.edu
	Ting Zheng
	University of Wisconsin-Madison	tzheng39@wisc.edu
	Volker Radeloff
	University of Wisconsin-Madison	radeloff@wisc.edu
	Kyle Kovach
	University of Wisconsin-Madison	kyle.kovach@wisc.edu
	Shuwen Liu
	University of Wisconsin-Madison	shuwen.liu@wisc.edu