Estimation of crop gross primary production (GPP): I. impact of MODISobservation footprint and impact of vegetation BRDF characteristics

Authors

Qingyuan Zhang, Unversities Space Research AssociationFollow
Yen-Ben Cheng, Earth Resources Technology, Inc.
Alexei I. Lyapustin, Climate and Radiation Laboratory
Yujie Wang, University of Maryland Baltimore County
Xiangming Xiao, University of Oklahoma
Andrew E. Suyker, University of Nebraska - LincolnFollow
Shashi Verma, University of Nebraska - LincolnFollow
Bin Tan, Sigma Space Corporation
Elizabeth M. Middleton, Biospheric Sciences LaboratoryFollow

Date of this Version

2014

Citation

Agricultural and Forest Meteorology 191 (2014) 51–63, http://dx.doi.org/10.1016/j.agrformet.2014.02.002 0168-1923

Comments

U. S. government work.

Abstract

Accurate estimation of gross primary production (GPP) is essential for carbon cycle and climate change studies. Three AmeriFlux crop sites of maize and soybean were selected for this study. Two of the sites were irrigated and the other one was rainfed. The normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the green band chlorophyll index (CI_green), and the green band wide dynamic range vegetation index (WDRVI_green) were computed from the moderate resolution imaging spectroradiometer (MODIS) surface reflectance data. We examined the impacts of the MODIS observation footprint and the vegetation bidirectional reflectance distribution function (BRDF) on crop daily GPP estimation with the four spectral vegetation indices (VIs - NDVI, EVI, WDRVI_green and CI_green) where GPP was predicted with two linear models, with and without offset: GPP = a × VI × PAR and GPP = a × VI × PAR + b.Model performance was evaluated with coefficient of determination (R2), root mean square error (RMSE),and coefficient of variation (CV). The MODIS data were filtered into four categories and four experiments were conducted to assess the impacts. The first experiment included all observations. The second experiment only included observations with view zenith angle (VZA) ≤ 35◦to constrain growth of the footprint size,which achieved a better grid cell match with the agricultural fields. The third experiment included only forward scatter observations with VZA ≤ 35◦. The fourth experiment included only backscatter observations with VZA ≤ 35◦. Overall, the EVI yielded the most consistently strong relationships to daily GPP under all examined conditions. The model GPP = a × VI × PAR + b had better performance than the mode lGPP = a × VI × PAR, and the offset was significant for most cases. Better performance was obtained for the irrigated field than its counterpart rainfed field. Comparison of experiment 2 vs. experiment 1 was usedto examine the observation footprint impact whereas comparison of experiment 4 vs. experiment 3 was used to examine the BRDF impact. Changes in R², RMSE,CV and changes in model coefficients “a” and “b”(experiment 2 vs. experiment 1; and experiment 4 vs. experiment 3) were indicators of the impacts. The second experiment produced better performance than the first experiment, increasing R²(↑0.13) and reducing RMSE (↓0.68 g C m⁻²d⁻¹) and CV (↓9%). For each VI, the slope of GPP = a × VI × PAR in the second experiment for each crop type changed little while the slope and intercept of GPP = a × VI × PAR + b varied field by field. The CI_green was least affected by the MODIS observation footprint in estimating crop daily GPP (R2, ↑0.08; RMSE, ↓0.42 g C m⁻²d⁻¹; and CV, ↓7%). Footprint most affected the NDVI (R2, ↑0.15; CV,↓10%) and the EVI (RMSE, ↓0.84 g C m⁻²d⁻¹). The vegetation BRDF impact also caused variation of model performance and change of model coefficients. Significantly different slopes were obtained for forward vs. backscatter observations, especially for the CI_green and the NDVI. Both the footprint impact and the BRDF impact varied with crop types, irrigation options, model options and VI options.