Remote estimation of gross primary productivity in crops using MODIS 250 m data

In this study, a simple model was developed to estimate crop gross primary productivity (GPP) using a product of chlorophyll-related vegetation index, retrieved from MODIS 250 m data, and potential photosynthetically active radiation (PAR). Potential PAR is incident photosynthetically active radiation under a condition of minimal atmospheric aerosol loading. This model was proposed for GPP estimation based entirely on satellite data, and it was tested in maize and soybean, which are contrasting crop types different in leaf structures and canopy architectures, under different crop managements and climatic conditions. The model using MODIS 250 m data, which brings high temporal resolution and moderate spatial resolution, was capable of estimating GPP accurately in both irrigated and rainfed croplands in three Nebraska AmeriFlux sites during growing seasons 2001 through 2008. Among the MODIS-250 m retrieved indices tested, enhanced vegetation index (EVI) and wide dynamic range vegetation index (WDRVI) were the most accurate for GPP estimation with coefficients of variation below 20% in maize and 25% in soybean. It was shown that the developed model was able to accurately detect GPP variation in crops where total chlorophyll content is closely tied to seasonal dynamic of GPP.

► Chlorophyll content and potential PAR were used to estimate crop GPP.
► Model used NDVI, EVI and WDRVI retrieved from MODIS 250 m products.
► Model was able to accurately estimate crop GPP with high temporal resolution.
► Using potential PAR reduced uncertainty of GPP estimates compared to incident PAR.