GEDI L4C Footprint Level Waveform Structural Complexity Index Released
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Summary
This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4C (L4C) Version 2 predictions of the Waveform Structural Complexity Index (WSCI) and estimates of prediction intervals for each footprint estimate at 95% confidence. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI04_C is the same as in the GEDI02_A product. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-17 to 2023-03-16. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth’s surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint WSCI was derived from XGBoost regression models relating simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to a 3D structural complexity metric calculated from matched Airborne laser Scanning (ALS) point clouds. Four global WSCI models were trained on a plant functional type (PFT) basis (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the WSCI estimates, including the associated uncertainty metrics, quality flags, and other information about the GEDI L2A waveform for this selected algorithm setting group. Additional model outputs include WSCI predictions for each of the six GEDI L2A algorithm setting groups and associated prediction intervals. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups. The data are provided in HDF5 format.There are 74,860 data files in HDF5 format (.h5) with this dataset.