Natural Resources, School of


Date of this Version



Remote Sensing of Environment 115 (2011), pp. 3091–3101; doi:10.1016/j.rse.2011.06.015


Copyright © 2011 Elsevier Inc. Used by permission.


Accurate assessment of temporal changes in gross primary production (GPP) is important for carbon budget assessments and evaluating the impact of climate change on crop productivity. The objective of this study was to devise a simple remote sensing- based GPP model to quantify daily GPP of maize. In the model, (1) daily shortwave radiation (SW), derived from the reanalysis data (North American Land Data Assimilation System; NLDAS-2) and (2) smoothed Wide Dynamic Range Vegetation Index (WDRVI) data, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations were used as proxy variables of the incident photosynthetically active radiation (PAR) and the total canopy chlorophyll content, respectively. The model was calibrated and validated by using tower-based CO2 flux observations over an 8-year period (2001 to 2008) for one rainfed and two irrigated sites planted to maize as part of the Carbon Sequestration Program at the University of Nebraska–Lincoln. The results showed the temporal features of the product SW*WDRVI closely related to the temporal GPP variations in terms of both daily variations and seasonal patterns. The simple GPP model was able to predict the daily GPP values and accumulated GPP values of maize with high accuracy.