• Spatiotemporal patterns of Spartina alterniflora belowground biomass (BGB) are important for evaluating salt marsh resiliency. To solve this, we created the BERM (Belowground Ecosystem Resiliency Model), which estimates monthly BGB (30-m spatial resolution) from freely available data such as Landsat-8 and Daymet climate summaries.
• 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 (R²) was 0.62–0.77. Testing data results were unbiased across BGB values and were positively correlated with ground-truth data across all sites and years (r = 0.56–0.82 and 0.45–0.95, respectively).
• BERM can estimate BGB within Spartina alterniflora salt marshes where environmental parameters are within the training data range, and can be readily extended through a reproducible workflow. This provides a powerful approach for evaluating spatiotemporal BGB and associated ecosystem function.

中文翻译：

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BERM：用于估计互花米草地下生物量的地下生态系统弹性模型

• 互花米草地下生物量 (BGB) 的时空模式对于评估盐沼恢复力很重要。为了解决这个问题，我们创建了 BERM（地下生态系统弹性模型），该模型根据 Landsat-8 和 Daymet 气候摘要等免费可用数据估算每月 BGB（30 米空间分辨率）。
• 我们的建模框架依赖于极端梯度提升，并使用来自乔治亚州四个盐沼的实地观测作为地面实况数据。模型预测因子包括估计的潮汐淹没、海拔、叶面积指数、叶氮、叶绿素、地表温度、物候和气候数据。最终模型包括 33 个变量，最重要的变量是海拔、前四个月的蒸气压、前五个月的归一化差异植被指数 (NDVI) 和淹没。
• 来自测试数据的 BGB 均方根误差为 313 g m ^-2（现场数据范围的 11%），解释方差 ( R ² ) 为 0.62–0.77。测试数据结果在 BGB 值之间是无偏的，并且与所有站点和年份的地面实况数据呈正相关（ 分别为r = 0.56-0.82 和 0.45-0.95）。
• BERM 可以估计互花米草盐沼中的BGB，其中环境参数在训练数据范围内，并且可以通过可重复的工作流程轻松扩展。这为评估时空 BGB 和相关的生态系统功能提供了一种强大的方法。