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 CO 2 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.
Highlights
The accurate assessment of vegetation gross primary productivity (GPP) is of great importance for regional and global studies of terrestrial ecosystem carbon budgets (Gitelson et al, 2006; Peng & Gitelson, 2011; Wu, Niu, & Gao, 2012), and it plays a significant role in dynamic responses of terrestrial ecosystem carbon cycling to global climate change (Fang, Yu, & Qi, 2015; Fang & Zhang, 2013; Shen & Fang, 2014)
This study further demonstrated that based on light use efficiency (LUE) principles, a CCCcanopy algorithm derived from field spectrometry measurements through in combination with an algorithm of fAPARgreen and PAR from meteorological observations could be used to estimate GPP in maize agricultural ecosystems
This study investigated remote estimation of LUE through estimating CCCcanopy based on field measurements of spectral reflectance, Chl, leaf area index (LAI), and ecosystem CO2 fluxes as well as micrometeorological factors conducted during the entire growing season for a maize canopy
Summary
The accurate assessment of vegetation gross primary productivity (GPP) is of great importance for regional and global studies of terrestrial ecosystem carbon budgets (Gitelson et al, 2006; Peng & Gitelson, 2011; Wu, Niu, & Gao, 2012), and it plays a significant role in dynamic responses of terrestrial ecosystem carbon cycling to global climate change (Fang, Yu, & Qi, 2015; Fang & Zhang, 2013; Shen & Fang, 2014). How to effectively relate CO2 flux observations with remote sensing techniques at the site level and to implement repetitive observations of CO2 flux over extensive spatial areas are becoming critical challenges for assessing global carbon budgets and monitoring ecosystem dynamical processes. The key for addressing these questions lies in the development of remote sensing-b ased ecosystem process models at broad spatial scales that can be effectively and quantitatively parameterized and validated by CO2 fluxes at site level.
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