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Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”

Dataset ·
DOI:https://doi.org/10.15485/2326012· OSTI ID:2326012
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  1. Lawrence Berkeley National Laboratory; Lawrence Berkeley National Laboratory
  2. Lawrence Berkeley National Laboratory
  3. Pacific Northwest National Laboratory (PNNL)

The package contains the data layers used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”. The study aims to use multi-source remote sensing and GIS datasets to investigate the spatial heterogeneity and identify spatial zones with similar environmental characteristics and understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. We employed unsupervised hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, then determined the main driving factors using Random Forest regression and SHapley Additive exPlanations (SHAP). Spatial data layers include soil respiration, kernel Normalized Difference Vegetation Index (kNDVI) computed from Harmonized Landsat 8 and Sentinel-2 time series, climate variables from the Daymet dataset, land cover, biodiversity, topographical metrics, soil property, and tidal elevation.

Research Organization:
Environmental System Science Data Infrastructure for a Virtual Ecosystem; COMPASS-FME
Sponsoring Organization:
U.S. DOE > Office of Science > Biological and Environmental Research (BER)
DOE Contract Number:
AC02-05CH11231
OSTI ID:
2326012
Country of Publication:
United States
Language:
English