Research

 1. Remote sensing of vegetation productivity using the solar-induced chlorophyll fluorescence (SIF).

    1. SIF offers an excellent opportunity to understand vegetation productivity from leaf, canopy, to the global scale. Our lab focuses on the measurements and simulation of SIF. See Yang et al., 2015; Lee et al., 2015; and Yang et al. in press in GCB.Below we show that SIF exhibits similar seasonal variations as GPP. Satellite SIF shows a similar seasonal pattern as the ground measurements.

GRL-SIF-GPP-1GOME2

2. Airborne remote sensing and drone-based remote sensing.

    We use remote sensing onboard airplane and Unmanned Aircraft System (UAS) to study the spatial and temporal variations of vegetation photosynthesis, temperature, and leaf traits. We use spectrometers, thermal cameras, and regular cameras.

 

3. Vegetation phenology and the terrestrial carbon, water, energy balance.

        1.  We use satellite remote sensing, digital repeat cameras, hyperspectral remote sensing, and biochemical methods to understand the impact of climate change on vegetation phenology and related changes in terrestrial carbon, water and energy cycles. See Yang et al. 2012, Yang et al., 2014, and Yang et al., 2016.
          Clockwise: Seasonal variations of key leaf traits including leaf nitrogen, carbon, and leaf mass per area (LMA) (Yang et al., 2016); Leaf spectroscopic seasonality (Yang et al., 2016); Leaf canopy phenology recorded from digital repeat photography (Yang et al., 2014); Canopy color is asynchronous with leaf chemistry (Yang et al., 2014).

LeafTraitsLeafSpectra

screen-shot-2016-12-10-at-9-48-22-amfig-1_image-example