Spartina alterniflora above- and belowground biomass predictions and inundation intensity as estimated by the Belowground Ecosystem Resiliency Model for U.S. Georgia marshes from 2014 to 2023.

We applied the Belowground Ecosystem Resiliency Model (BERM) to estimate monthly aboveground biomass (AGB) and belowground biomass (BGB) in U.S. Georgia Spartina alterniflora marshes from 2014 to 2023 at 30 m scale. This application involved BERM version 2.0 (https://doi.org/10.5281/zenodo.13306821), which was built using data in the PLT-GCET-2308 dataset (https://dx.doi.org/10.6073/pasta/4a0b715104849d98320fcc34e7cd63a4). Data sources for BERM application included Landsat-8/9, NOAA CO-OPS Station ID: 8670870, Daymet, and USGS 3DEP 2018 DEM. Download and processing steps are described in the BERM code and in metadata methods section. Specific descriptions of data processing are available in model code: https://doi.org/10.5281/zenodo.13306821. Data provided here include model output of AGB estimates, BGB estimates, and calculated inundation intensity. See "Data reporting" method in the metadata for description of data files.
For logisitical purposes here we present only select data from the model input and output. All model input data sources as listed in the abstract are publicly available. Model calibration data and code are published as well. Additional predictions not published here include foliar chlorophyll, foliar nitrogen, and leaf area index.

本研究采用地下生态系统恢复力模型（Belowground Ecosystem Resiliency Model, BERM），以30米空间分辨率对2014—2023年美国佐治亚州互花米草盐沼的月度地上生物量（aboveground biomass, AGB）与地下生物量（belowground biomass, BGB）进行了估算。本次研究应用的BERM版本为2.0（https://doi.org/10.5281/zenodo.13306821），该模型基于PLT-GCET-2308数据集（https://dx.doi.org/10.6073/pasta/4a0b715104849d98320fcc34e7cd63a4）构建。BERM应用的数据源包括陆地卫星8号/9号（Landsat-8/9）、美国国家海洋和大气管理局海洋观测系统（NOAA CO-OPS）站点ID：8670870、Daymet数据集以及美国地质调查局2018年3DEP数字高程模型（USGS 3DEP 2018 DEM）。数据下载与处理步骤详见BERM代码及元数据方法章节，具体的数据处理说明可在模型代码中获取：https://doi.org/10.5281/zenodo.13306821。本数据集提供模型输出的AGB估算结果、BGB估算结果以及计算得到的淹水强度。数据文件的详细描述可参见元数据中的"数据报告"方法。
出于数据组织与呈现的便利性考虑，本次仅展示了部分模型输入与输出数据。前文摘要中列出的所有模型输入数据源均为公开可用资源。模型校准数据与代码也已正式发表。本次未公开的额外预测结果包括叶片叶绿素含量、叶片氮含量以及叶面积指数。