EUSFlux: Mapping Forest Carbon Uptake in the Eastern US from Upscaled Flux Tower Data

This repository provides a wall-to-wall, spatially explicit dataset of Net Ecosystem Productivity (NEP) across eastern United States forests at 500 m spatial resolution and 16-day temporal resolution, spanning 2003–2023. NEP is estimated using a Random Forest model trained on eddy covariance observations from AmeriFlux and NEON flux tower networks and applied to gridded remote sensing, solar-induced fluorescence (SIF), and meteorological predictor variables. Predictor variables include MODIS-derived surface reflectance bands (MOD09A1 Bands 1–7), NDVI (MOD13A1), LAI and FPAR (MOD15A2H), NIRv (computed as (NDVI − 0.08) × NIR), GOSIF solar-induced fluorescence, and Daymet v4 meteorological variables (Tmin, shortwave radiation, precipitation, daylength, and log-transformed VPD). All predictors are temporally composited to 16-day intervals and harmonized to a 500 m spatial grid. Feature selection was performed using permutation importance across 100 iterations of 5-fold spatial cross-validation; SHAP analysis provides model interpretability. The final model uses 11 predictor variables: Category (forest type), month, NDVI, SIF, surface reflectance Bands 2, 6, and 7, LAI, FPAR, daylength, and shortwave radiation, and Tmin. The Zenodo repository includes: • The preprocessed flux tower training dataset (NEE_flux_unh_sif_2.csv), containing 16-day site-level NEE observations and co-located predictor variables from AmeriFlux/NEON sites in eastern US forests; • An independent evaluation dataset of monthly FluxCom NEE estimates at seven held-out flux tower test sites (test_sites_monthly_fluxComNEE_2003_2021.csv; column NEE_gC_m2_d1); • An aboveground carbon benchmark raster from Harris et al. (2021) / Global Forest Watch, masked to the eastern US study domain (GFW_masked.tif); • The study area boundary shapefile (StudyArea.shp); • Seven Python notebooks implementing the full analysis workflow, from feature selection through temporal anomaly detection; • Annual NEP GeoTIFFs (YYYY_yearly_sum.tif, 2003–2023) and a 20-year mean map (NEP_20yr_Mean_Map.tif) at 500 m resolution; NEP is reported in units of gC m⁻² year⁻¹ in annual rasters, with positive values indicating net carbon uptake and negative values indicating net carbon release. The sign convention follows NEP = −NEE; flux tower data and intermediate prediction rasters are in NEE convention and are sign-flipped in the analysis scripts. The Zenodo repository has been updated to include comprehensive file-level and variable-level metadata describing the contents, formats, column names, units, and provenance of all shared data files and code. A complete README is provided within the deposit. Predictor variables are derived from the following external datasets, which are not included in this deposit and must be obtained separately: MODIS MOD09A1, MOD13A1, and MOD15A2H (NASA LP DAAC / Google Earth Engine); GOSIF SIF (University of New Hampshire); Daymet v4 (ORNL DAAC / Google Earth Engine); AmeriFlux/NEON flux tower observations; FLUXCOM RS+METEO (Max Planck Institute for Biogeochemistry); NLCD 2010 (USGS); and a forest stand age raster for 2010.本仓库提供了覆盖美国东部森林的全覆盖空间显式生态系统净生产力（Net Ecosystem Productivity, NEP）数据集，空间分辨率为500米，时间分辨率为16天，时间跨度为2003年至2023年。NEP的估算基于训练自AmeriFlux与NEON通量塔网络涡度协方差（eddy covariance）观测数据的随机森林（Random Forest）模型，将其应用于网格化遥感数据、日光诱导叶绿素荧光（solar-induced fluorescence, SIF）以及气象预测变量完成。 所用预测变量包括MODIS衍生的地表反射率波段（MOD09A1第1-7波段）、归一化植被指数（Normalized Difference Vegetation Index, NDVI，MOD13A1产品）、叶面积指数（Leaf Area Index, LAI）与光合有效辐射吸收比率（Fraction of Photosynthetically Active Radiation, FPAR，MOD15A2H产品）、近红外植被指数NIRv（计算式为(NDVI − 0.08) × NIR）、GOSIF日光诱导叶绿素荧光数据，以及Daymet v4气象变量：最低气温（Tmin）、短波辐射、降水量、日长以及对数变换后的水汽压差（Vapor Pressure Deficit, VPD）。所有预测变量均被合成为16天时间步长，并统一匹配至500米空间网格。特征选择通过100次5折空间交叉验证的置换重要性方法完成；SHAP分析用于提升模型可解释性。最终模型采用11个预测变量：森林类型类别、月份、NDVI、SIF、地表反射率第2、6、7波段、LAI、FPAR、日长、短波辐射以及Tmin。 该Zenodo存档包含以下内容： • 预处理后的通量塔训练数据集（NEE_flux_unh_sif_2.csv），包含美国东部森林区域AmeriFlux与NEON通量塔站点的16天尺度站点级净生态系统交换（Net Ecosystem Exchange, NEE）观测数据与匹配的预测变量； • 独立评估数据集：来自7个预留通量塔测试站点的月度FluxCom NEE估算数据（test_sites_monthly_fluxComNEE_2003_2021.csv，对应列名为NEE_gC_m2_d1）； • 来自Harris等人(2021)与全球森林观察（Global Forest Watch）的地上碳基准栅格，已掩膜至美国东部研究区域（GFW_masked.tif）； • 研究区域边界形状文件（StudyArea.shp）； • 7份Python脚本笔记，实现从特征选择到时间异常检测的完整分析流程； • 2003年至2023年的年度NEP GeoTIFF文件（YYYY_yearly_sum.tif），以及500米分辨率的20年平均NEP分布图（NEP_20yr_Mean_Map.tif）。 年度栅格中的NEP单位为gC m⁻² year⁻¹，正值表示净碳吸收，负值表示净碳释放。该符号约定遵循NEP = −NEE；通量塔原始数据与中间预测栅格采用NEE符号约定，需在分析脚本中完成符号翻转。 该Zenodo存档已更新，包含完整的文件级与变量级元数据，涵盖所有共享数据文件与代码的内容、格式、列名、单位及溯源信息。存档中附带完整的README文档。 本存档未包含以下预测变量来源的外部数据集，需单独获取：MODIS MOD09A1、MOD13A1及MOD15A2H产品（NASA LP DAAC / Google Earth Engine）；GOSIF SIF数据（新罕布什尔大学）；Daymet v4气象数据（ORNL DAAC / Google Earth Engine）；AmeriFlux与NEON通量塔观测数据；FLUXCOM RS+METEO数据（马克斯·普朗克生物地球化学研究所）；NLCD 2010产品（美国地质调查局USGS）；以及2010年森林林龄栅格数据。