Assessing uncertainties related to the use of satellite remote sensing indices to estimate Gross Primary Production

Abstract

Methods to quantify Gross Primary Production (GPP) are classified into two categories: Eddy Covariance techniques (EC) and satellite data-driven. EC techniques can measure carbon fluxes directly, albeit with spatial constraints. Satellite data-driven methods are promising because they overcome spatial constraints associated with EC techniques. However, satellite- driven products have potentially greater uncertainty than EC methods for GPP estimation such as mixed pixels, cloud cover, and the ability of the sensor to retrieve vegetation under sat- uration conditions in high biomass environments. Therefore, an effort to analyze and quantify the uncertainty of GPP products derived from satellite platforms is needed. This study quan- tifies the uncertainty of commonly used satellite vegetation indices such as Normalized Dif- ference Vegetation Index (NDVI), Enhance Vegetation Index (EVI), Chlorophyll/Carotenoid Index (CCI), and Near-Infrared Reflectance Index (NIRv) for GPP estimation compared with direct methods such as EC measurements. We conduct this study on three different sites: the University of Michigan Biological Station (USA), the Borden Forest Research Station flux-site (Canada), and Bartlett Experimental Forest (USA) using traditional regression methods and ML approaches.