Parameterizing ecosystem light use efficiency and water use efficiency to estimate maize gross primary production and evapotranspiration using MODIS EVI

•EVI was explicitly used as a measure of ecosystem light use efficiency (ELUE).•EVI was explicitly used as a measure of ecosystem water use efficiency (EWUE).•Parameterized ELUE model simulated tower-based GPP with reasonable accuracy.•Parameterized EWUE model simulated tower-based ET with reasonabl...

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Bibliographic Details
Published in:Agricultural and forest meteorology 2016-05, Vol.222, p.87-97
Main Authors: Wagle, Pradeep, Gowda, Prasanna H., Xiao, Xiangming, KC, Anup
Format: Article
Language:English
Subjects:
Artificial neural network
carbon
climatic factors
corn
ecosystems
Eddy covariance
Empirical and statistical models
enterotoxigenic Escherichia coli
evapotranspiration
Glycine max
irrigation
methodology
moderate resolution imaging spectroradiometer
photosynthetically active radiation
prediction
primary productivity
Remote sensing
Seasonal and interannual variability
seasonal variation
soybeans
spatial data
temperature
uncertainty
vegetation index
Vegetation indices
water management
water use efficiency
Zea mays
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Summary:•EVI was explicitly used as a measure of ecosystem light use efficiency (ELUE).•EVI was explicitly used as a measure of ecosystem water use efficiency (EWUE).•Parameterized ELUE model simulated tower-based GPP with reasonable accuracy.•Parameterized EWUE model simulated tower-based ET with reasonable accuracy.•This study offers new methods for predicting GPP and ET. Quantifying global carbon and water balances requires accurate estimation of gross primary production (GPP) and evapotranspiration (ET), respectively, across space and time. Models that are based on the theory of light use efficiency (LUE) and water use efficiency (WUE) have emerged as efficient methods for predicting GPP and ET, respectively. Currently, LUE and WUE estimates are obtained from biome-specific look-up tables and coarse resolution remote sensing data with large uncertainties. The major objective of this study was to parameterize eddy covariance tower-based ecosystem LUE (ELUEEC), defined as the ratio of tower-based GPP (GPPEC) to photosynthetically active radiation (PAR), and ecosystem WUE (EWUEEC), defined as the ratio of GPPEC to tower-based ET (ETEC), using the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) for predicting maize (Zea mays L.) GPP and ET, respectively. Three adjacent AmeriFlux maize sites with different rotations (continuous maize vs. annual rotation of maize and soybean, Glycine max L.) and water management practices (rainfed vs. irrigated) located near Mead, NE, USA were selected. The EVI tracked the seasonal variations of ELUEEC (R2=0.83) and EWUEEC (R2=0.74) across sites, indicating that EVI can be explicitly used as a measure of ELUEEC and EWUEEC. The predicted GPP (GPPELUE) using the parameterized ELUE model correlated well with GPPEC (slope=1.0, R2=0.83, and RMSE=2.85gCm−2d−1) and was significantly improved when compared to widely used models that estimate GPP by integrating EVI and climate variables (Greenness and Radiation, Temperature and Greenness, and Vegetation Index) and the standard MOD17 GPP product. Similarly, the predicted ET (ETEWUE) using the parameterized EWUE correlated well with ETEC (slope=1.02, R2=0.62, and RMSE=0.83mmET−1) and was significantly improved when compared to the standard MOD16 ET product. Preliminary data demonstrate that ELUE and EWUE can be parameterized using EVI, offering new methods for predicting GPP and ET.
ISSN:0168-1923
1873-2240
DOI:10.1016/j.agrformet.2016.03.009