Deriving a light use efficiency estimation algorithm using in situ hyperspectral and eddy covariance measurements for a maize canopy in Northeast China. Issue 13 (23rd May 2017)

• Record Type: Journal Article
• Authors: Zhang, Feng; Zhou, Guangsheng
• Is Part Of: Ecology and evolution. Volume 7:Issue 13(2017:Jul.)
• Page Start: 4735
• Page End: 4744
• Publication Date: 2017-05-23
• Subjects: canopy chlorophyll content -- eddy covariance -- hyperspectral remote sensing -- light use efficiency -- spectral vegetation indices; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.3051
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: We estimated the light use efficiency ( LUE ) via vegetation canopy chlorophyll content ( CCC canopy ) based on in situ measurements of spectral reflectance, biophysical characteristics, ecosystem CO2 fluxes and micrometeorological factors over a maize canopy in Northeast China. The results showed that among the common chlorophyll‐related vegetation indices (VIs), CCC canopy had the most obviously exponential relationships with the red edge position (REP) ( R 2 = .97, p < .001) and normalized difference vegetation index (NDVI) ( R 2 = .91, p < .001). In a comparison of the indicating performances of NDVI, ratio vegetation index (RVI), wide dynamic range vegetation index (WDRVI), and 2‐band enhanced vegetation index (EVI2) when estimating CCC canopy using all of the possible combinations of two separate wavelengths in the range 400−1300 nm, EVI2 [1214, 1259] and EVI2 [726, 1248] were better indicators, with R 2 values of .92 and .90 ( p < .001). Remotely monitoring LUE through estimating CCC canopy derived from field spectrometry data provided accurate prediction of midday gross primary productivity ( GPP ) in a rainfed maize agro‐ecosystem ( R 2 = .95, p < .001). This study provides a new paradigm for monitoring vegetation GPP based on the combination of LUE models with plant physiological properties. Abstract : Vegetation light use efficiency is a key parameter for the light use efficiency models. We addressed the question of estimating light use efficiency through estimation of canopy chlorophyll content derived from in situ hyperspectral measurement. This study provides a new paradigm for monitoring vegetation gross primary productivity based on the combination of light use efficiency models with plant physiological properties.