Merge pull request #74 from ronnyhdez/T71

Tables labels corrections

abstract.qmd

@@ -1,3 +1,3 @@
 # Abstract {.unnumbered}
 
-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 of spatial constraints. Satellite data-driven methods are promising because they overcome spatial constraints associated with EC techniques. However, there are challenges associated with an increase in uncertainty when estimating GPP from satellite-driven products such as mixed pixels, cloud cover, and the ability of the sensor to retrieve vegetation under saturation conditions in high biomass environments. Therefore an effort to analyze and quantify the uncertainty of GPP products derived from satellite platforms is needed. Here we present how commonly used satellite vegetation indices (NDVI, EVI, CCI, kNDVI, and NIRv) with different spatial resolutions can impact the uncertainty in the GPP estimation compared with direct methods such as eddy covariance measurements. We conduct this study on three different sites: University of Michigan Biological Station (USA), the Borden Forest Research Station flux-site (Canada) and Bartlett Experimental Forest (USA).
+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 of spatial constraints. Satellite data-driven methods are promising because they overcome spatial constraints associated with EC techniques. However, there are challenges associated with an increase in uncertainty when estimating GPP from satellite-driven products such as mixed pixels, cloud cover, and the ability of the sensor to retrieve vegetation under saturation conditions in high biomass environments. Therefore an effort to analyze and quantify the uncertainty of GPP products derived from satellite platforms is needed. Here we present how commonly used satellite vegetation indices (NDVI, EVI, CCI, and NIRv) with different spatial resolutions can impact the uncertainty in the GPP estimation compared with direct methods such as eddy covariance measurements. We conduct this study on three different sites: University of Michigan Biological Station (USA), the Borden Forest Research Station flux-site (Canada) and Bartlett Experimental Forest (USA).