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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Forage and Livestock Production Research » Research » Publications at this Location » Publication #325573

Research Project: Integrated Forage Systems for Food and Energy Production in the Southern Great Plains

Location: Forage and Livestock Production Research

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

Author
item Wagle, Pradeep - University Of Oklahoma
item Gowda, Prasanna
item Xiao, Xiangming - University Of Oklahoma
item Anup, K - Oklahoma State University

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/18/2016
Publication Date: 3/25/2016
Citation: Wagle, P., Gowda, P., Xiao, X., Anup, K.C. 2016. Parameterizing ecosystem light use efficiency and water use efficiency to estimate maize gross primary production and evapotranspiration using MODIS EVI. Agricultural and Forest Meteorology. 222:87-97.

Interpretive Summary: Accurate estimation of gross primary production (GPP, total amount of carbon gain by ecosystem via photosynthesis) and evapotranspiration (ET, total amount of water loss by the ecosystem via transpiration and evaporation) across space and time is necessary to quantify global carbon and water balances, respectively. The current process-based, remote sensing-based, and physical models for estimating GPP and ET are complex and require advanced knowledge of the area. In this study, we developed simple predictive models using climate data and moderate resolution (500 m) remotely sensed vegetation index for estimating daily GPP and ET of maize ecosystems in Nebraska. Results indicated that the performance of the proposed simple predictive models is comparable (even better) with complex process-based, remote sensing-based, and physical models. However, further validation is needed for additional independent maize sites.

Technical Abstract: 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 = 2.85 g C m-2 d-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 = 0.83 mm ET-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.