Overview of the Simple Biosphere Model (SiB4) that estimates carbon fluxes among the atmosphere, vegetation, and soils. Input information is shown in yellow boxes. These datasets include a selection of the output variables (blue boxes). Source: Haynes et al. (2020)
Global terrestrial model outputs predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution, 2000-2018.
An area of the Mississippi River Delta (Terrebonne and St. Mary Parishes, LA, USA) showing the largest present-day amount of tidal vegetation biomass in the conterminous US. (Source Louisiana_biomass_2015.tif).
Aboveground tidal marsh biomass estimates for six estuarine regions of the conterminous United States.
Examples of spatial patterns of reconstructed SIF at 4 km resolution. Only pixels with a fraction of the specific vegetation type larger than 0.1 are shown. For Total SIF, only pixels with a fraction of corn, soybean, grass/pasture, and forest larger than 0.8 are displayed.
Estimated solar-induced chlorophyll fluorescence (SIF) of specific vegetation types and total SIF available at 4-km resolution.
Riverine N2O emission estimates from headwater streams (a, c, and e) and high-order rivers (b, d, and f) for the 1900s, 1960s, and 2000s. The right panel shows the latitudinal distribution of riverine N2O emissions with the uncertainty range as the standard deviation (shaded areas).
Modeled estimates of annual NO2 emissions for two sets of global rivers and streams covering years 1900-2016 available.
Example of average daily gross primary production (GPP) per m2 at 250 m resolution shown for wetlands in the North and South Ten Thousand Islands in the Florida Everglades. Mapped values are an average of all 16-day periods from 2000-2019. Source: Feagin et al., 2020.
Tidal wetland gross primary production estimates across the Conterminous US derived with the spatially-explicit Blue Carbon model.
Estimated aboveground biomass (Mg/ha) for Sonoma County at 30 m spatial resolution with the 5th-95th percentile range and the standard deviation (SD) of per-pixel biomass estimates shown in the top left and bottom left, respectively.
A new dataset used a parametric modeling approach to estimate biomass from airborne LiDAR data and field measurements.