Salt marshes play a crucial role in coastal biogeochemical cycles and provide unique ecosystem services. Salt marsh biomass, which can strongly influence such services, varies over time in response to hydrologic conditions and other environmental drivers. We used gap-filled monthly observations of
Spatiotemporal patterns of Our modeling framework relied on extreme gradient boosting, and used field observations from four Georgia salt marshes as ground‐truth data. Model predictors included estimated tidal inundation, elevation, leaf area index, foliar nitrogen, chlorophyll, surface temperature, phenology, and climate data. The final model included 33 variables, and the most important variables were elevation, vapor pressure from the previous four months, Normalized Difference Vegetation Index (NDVI) from the previous five months, and inundation. Root mean squared error for BGB from testing data was 313 g m−2(11% of the field data range), explained variance ( BERM can estimate BGB within
- Award ID(s):
- 1832178
- NSF-PAR ID:
- 10447577
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 232
- Issue:
- 1
- ISSN:
- 0028-646X
- Format(s):
- Medium: X Size: p. 425-439
- Size(s):
- ["p. 425-439"]
- Sponsoring Org:
- National Science Foundation
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