AbstractsBiology & Animal Science

Productivity in agricultural ecosystems is important to understand in terms of their role as a strong modifier of regional carbon balance, but also in their intended role of capturing carbon (energy) in the form of food products, e.g. agricultural yield. Gross primary production (GPP) of agricultural ecosystems is the amount of total carbon assimilated by the planted crops and the driver of useful biomass production. To assess the GPP of croplands, this study combines information from flux determinations with eddy covariance (EC) methodology, process-based modeling of carbon gain, and satellite remotely-sensed vegetation indices (VIs). The data is brought together synthetically for major crops found in agricultural landscapes of Gwangwon Province, South Korea, e.g., rice, soybean, maize, potato, and sugar beet as a surrogate for radish. The long term goal (beyond the current effort) is to utilize the results to assess carbon balances, agricultural production and yields in the landscape of Haean Catchment, South Korea, which has been the focus of research in the TERRECO project (see acknowledgement). This study focuses on relating two major variables determining GPP; leaf area index (LAI) of the crop and carboxylation capacity of the crop canopy (Vcuptake - as first defined by Owen et al. 2007), to MODIS remotely sensed vegetation indices (VIs). Success in deriving such relationships will allow GPP to be remotely determined over the seasonal course of crop development. The relationship to VIs of both LAI and Vcuptake were considered first by using the general regression approaches commonly applied in remote sensing studies, i.e., simple linear models or other statistical regression models. The results of GPP estimation from these general models were not adequate and led overall to underestimations. Therefore, a new alternative approach was developed to estimate LAI and Vcuptake that used consistent development curves for each crop, i.e., relies on consistent biological regulation of plant development. In this case, the remote sensing maximum in VIs is used to identify timing of phenological development at the observed location. Depending on the maximum in VIs, seasonal change in the critical variables for structure and crop physiology may be estimated by synthesizing data from EC studies at multiple sites for each crop. The relationship between observed GPP and modeled GPP based on the consistent development curves for LAI is remarkably improved over regression based values with R2 from 0.79 to 0.93. Modeled GPP based on the consistent development curve for both LAI and Vcuptake agreed with R2 from 0.76 to 0.92 (within the 95% confidence interval) at the rice paddy sites. In the case of dry-land crops, the relationship between measured and modeled GPP based on the consistent development curve for LAI showed significantly improved results with R2 from 0.61 to 0.93 (within the 95% confidence interval), while measured vs. modeled GPP based on the consistent development curve for both LAI and Vcuptake exhibited an R2…