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Estimation of Crop Gross Primary Production (GPP): II. Do Scaled MODIS Vegetation Indices Improve Performance?
Agricultural and Forest Meteorology
  • Qingyuan Zhang, Universities Space Research Association
  • Yen-Ben Cheng, Biospheric Sciences Laboratory, NASA/Goddard Space Flight Center
  • Alexei I. Lyapustin, Climate and Radiation Laboratory, NASA/Goddard Space Flight Center
  • Yujie Wang, Biospheric Sciences Laboratory, NASA/Goddard Space Flight Center
  • Xiaoyang Zhang, South Dakota State University
  • Andrew Suyker, University of Nebraska - Lincoln
  • Shashi Verma, University of Nebraska - Lincoln
  • Yanmin Shuai
  • Elizabeth M. Middleton, Biospheric Sciences Laboratory, NASA/Goddard Space Flight Center
Document Type
Publication Version
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Publication Date
  • Daily GPP,
  • MODIS,
  • vegetation index,
  • fAPARchl
Satellite remote sensing estimates of gross primary production (GPP) have routinely been made using spectral vegetation indices (VIs) over the past two decades. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the green band Wide Dynamic Range Vegetation Index (WDRVIgreen), and the green band Chlorophyll Index (CIgreen) have been employed to estimate GPP under the assumption that GPP is proportional to the product of VI and photosynthetically active radiation (PAR) (where VI is one of four VIs: NDVI, EVI, WDRVIgreen, or CIgreen). However, the empirical regressions between VI*PAR and GPP measured locally at flux towers do not pass through the origin (i.e., the zero X–Y value for regressions). Therefore they are somewhat difficult to interpret and apply. This study investigates (1) what are the scaling factors and offsets (i.e., regression slopes and intercepts) between the fraction of PAR absorbed by chlorophyll of a canopy (fAPARchl) and the VIs and (2) whether the scaled VIs developed in (1) can eliminate the deficiency and improve the accuracy of GPP estimates. Three AmeriFlux maize and soybean fields were selected for this study, two of which are irrigated and one is rainfed. The four VIs and fAPARchl of the fields were computed with the MODerate resolution Imaging Spectroradiometer (MODIS) satellite images. The GPP estimation performance for the scaled VIs was compared to results obtained with the original VIs and evaluated with standard statistics: the coefficient of determination (R2), the root mean square error (RMSE), and the coefficient of variation (CV). Overall, the scaled EVI obtained the best performance. The performance of the scaled NDVI, EVI and WDRVIgreen was improved across sites, crop types and soil/background wetness conditions. The scaled CIgreen did not improve results, compared to the original CIgreen. The scaled green band indices (WDRVIgreen, CIgreen) did not exhibit superior performance to either the scaled EVI or NDVI in estimating crop daily GPP at these agricultural fields. The scaled VIs are more physiologically meaningful than original un-scaled VIs, but scaling factors and offsets may vary across crop types and surface conditions.
DOI of Published Version
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This article appeared in Agricultural and Forest Meteorology (2015) 200, doi: 10.1016/j.agrformet.2014.09.003

Citation Information
Qingyuan Zhang, Yen-Ben Cheng, Alexei I. Lyapustin, Yujie Wang, et al.. "Estimation of Crop Gross Primary Production (GPP): II. Do Scaled MODIS Vegetation Indices Improve Performance?" Agricultural and Forest Meteorology Vol. 200 (2015)
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