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Coastal salt marshes are biologically productive ecosystems that generate and sequester significant quantities of organic matter. Plant biomass varies spatially within a salt marsh and it is tedious and often logistically impractical to quantify biomass from field measurements across an entire landscape. Satellite data are useful for estimating aboveground biomass, however, high-resolution data are needed to resolve the spatial details within a salt marsh. This study used 3-m resolution multispectral data provided by Planet to estimate aboveground biomass within two salt marshes, North Inlet-Winyah Bay (North Inlet) National Estuary Research Reserve, and Plum Island Ecosystems (PIE) Long-Term Ecological Research site. The Akaike information criterion analysis was performed to test the fidelity of several alternative models. A combination of the modified soil vegetation index 2 (MSAVI2) and the visible difference vegetation index (VDVI) gave the best fit to the square root-normalized biomass data collected in the field at North Inlet (Willmott’s index of agreement d = 0.74, RMSE = 223.38 g/m2, AICw = 0.3848). An acceptable model was not found among all models tested for PIE data, possibly because the sample size at PIE was too small, samples were collected over a limited vertical range, in a different season, and from areas with variable canopy architecture. For North Inlet, a model-derived landscape scale biomass map showed differences in biomass density among sites, years, and showed a robust relationship between elevation and biomass. The growth curve established in this study is particularly useful as an input for biogeomorphic models of marsh development. This study showed that, used in an appropriate model with calibration, Planet data are suitable for computing and mapping aboveground biomass at high resolution on a landscape scale, which is needed to better understand spatial and temporal trends in salt marsh primary production.

Estimating Aboveground Biomass and Its Spatial Distribution in Coastal Wetlands Utilizing Planet Multispectral Imagery