N: 54 S: -54 E: 180 W: -180
Description
The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.
The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024.
The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI02_A product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.
The GEDI02_A data product contains 156 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (e.g., canopy vertical structure), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary.
Known Issues
- Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).
- Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this document for the correct RGT numbers.
- Known Issues: Section 8 of the User Guide provides additional information on known issues.
Version Description
Product Summary
Citation
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
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File Naming Convention
The file name begins with the Product Short Name (GEDI02_A), followed by the Julian Date and Time of Acquisition designated as YYYYDDDHHMMSS (2024333190440), the Orbit Number starting with the letter O (O33777), the Sub-Orbit Granule Number (03), Track Number (T03689), the Positioning and Pointing Determination System type where 00 is predict, 01 rapid, 02 and higher is final (02), the Product Generation Executables Version (004), the Granule Production Version (02), the Version Number (V002), and the Data Format (h5).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
Dataset Resources
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Unveiling the performance and influential factors of GEDI L2A for building height retrieval | Chen, Peimin, Huang, Huabing, Qin, Peng, Wu, Zhenbang, Wang, Zixuan, Liu, Chong, Dong, Na, Wang, Jie | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| The utility of dynamic forest structure from GEDI lidar fusion in tropical mammal species distribution models | Burns, Patrick, Kaszta, Zaneta, Cushman, Samuel A., Brodie, Jedediah F., Hakkenberg, Christopher R., Jantz, Patrick, Deith, Mairin, Luskin, Matthew Scott, Ball, James G. C., Mohd-Azlan, Jayasilan, Burslem, David F. R. P., Cheyne, Susan M., Haidir, Iding, Hearn, Andrew James, Slade, Eleanor, Williams, Peter J., Macdonald, David W., Goetz, Scott J. | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, LIDAR WAVEFORM, Terrain Elevation, Digital Elevation/Terrain Model (DEM) | |
| Species distribution models predict a future decline in urban avian biodiversity in response to changes in land use and climatic conditions | Buhrs, Malte, Rienow, Andreas, Zepp, Harald | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Environmental drivers of spatial variation in tropical forest canopy height: Insights from NASA's GEDI spaceborne LiDAR | Liu, Shaoqing, Csillik, Ovidiu, Ordway, Elsa M., Chang, Li-Ling, Longo, Marcos, Keller, Michael, Moorcroft, Paul R. | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Lightning, Lightning | |
| A fast and efficient method to estimate inland water levels using CYGNSS | Ma, Zhongmin, Zhang, Shuangcheng, Camps, Adriano, Park, Hyuk, Liu, Qi, Tan, Pengyuan, Wang, Changyang | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Radar Cross-Section, Radar Reflectivity | |
| A global dataset of forest regrowth following wildfires | Zang, Jinlong, Qiu, Feng, Zhang, Yongguang | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| A Global Evaluation of Radar-Derived Digital Elevation Models: SRTM | Simard, Marc, Denbina, Michael, Marshak, Charles, Neumann, Maxim | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| A global product of 150-m urban building height based on spaceborne | Ma, Xiao, Zheng, Guang, Xu, Chi, Moskal, L. Monika, Gong, Peng, Guo, Qinghua, Huang, Huabing, Li, Xuecao, Liang, Xinlian, Pang, Yong, Wang, Cheng, Xie, Huan, Yu, Bailang, Zhao, Bo, Zhou, Yuyu | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| A Separable Bootstrap Variance Estimation Algorithm for Hierarchical | Saarela, Svetlana, Healey, Sean P., Yang, Zhiqiang, Roald, BjrnEirik, Patterson, Paul L., Gobakken, Terje, Nsset, Erik, Hou, Zhengyang, McRoberts, Ronald E., Stahl, Goran | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Assessing vertical accuracy and spatial coverage of ICESat-2 and GEDI spaceborne lidar for creating global terrain models | Pronk, Maarten, Eleveld, Marieke, Ledoux, Hugo | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, LIDAR WAVEFORM, Terrain Elevation | |
| Calibrating Satellite Maps With Field Data for Improved Predictions of | May, Paul B., Finley, Andrew O. | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Ladder fuels rather than canopy volumes consistently predict wildfire | Hakkenberg, Christopher R., Clark, Matthew L., Bailey, Tim, Burns, Patrick, Goetz, Scott J. | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Integrating Multi-Source Satellite Data and Environmental Information in a U-Net Architecture for Canopy Height Mapping in French Guiana | Lahssini, Kamel, Baghdadi, Nicolas, Maire, Guerric Le, Fayad, Ibrahim, Vincent, Gregoire | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach | Schwartz, Martin, Ciais, Philippe, Ottle, Catherine, De Truchis, Aurelien, Vega, Cedric, Fayad, Ibrahim, Brandt, Martin, Fensholt, Rasmus, Baghdadi, Nicolas, Morneau, Francois, Morin, David, Guyon, Dominique, Dayau, Sylvia, Wigneron, Jean-Pierre | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| First results of the surface water ocean topography (SWOT) observations to rivers elevation profiles in the Cuvette Centrale of the Congo Basin | Normandin, Cassandra, Frappart, Frederic, Baghdadi, Nicolas, Bourrel, Luc, Pena Luque, Santiago, Ygorra, Bertrand, Kitambo, Benjamin, Papa, Fabrice, Riazanoff, Serge, Wigneron, Jean-Pierre | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM | |
| Hy-TeC: a hybrid vision transformer model for high-resolution and large-scale mapping of canopy height | Fayad, Ibrahim, Ciais, Philippe, Schwartz, Martin, Wigneron, Jean-Pierre, Baghdadi, Nicolas, de Truchis, Aurelien, d'Aspremont, Alexandre, Frappart, Frederic, Saatchi, Sassan, Sean, Ewan, Pellissier-Tanon, Agnes, Bazzi, Hassan | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM, VIEWING GEOMETRY, Terrain Elevation | |
| How to Find Accurate Terrain and Canopy Height GEDI Footprints in | Moudry, Vitezslav, Prosek, Jiri, Marselis, Suzanne, Maresova, Jana, Sarovcova, Eliska, Gdulova, Katerina, Kozhoridze, Giorgi, Torresani, Michele, Rocchini, Duccio, Eltner, Anette, Liu, Xiao, Potuckova, Marketa, Sedova, Adela, CrespoPeremarch, Pablo, Torralba, Jesus, Ruiz, Luis A., Perrone, Michela, Spatenkova, Olga, Wild, Jan | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, LIDAR WAVEFORM, Terrain Elevation | |
| How to get closer to actual forest stand height using GEDI? A case study in central European Scots pine stands | Krawczyk, Wojciech, Wezyk, Piotr | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM | |
| Human degradation of tropical moist forests is greater than previously estimated | Bourgoin, C., Ceccherini, G., Girardello, M., Vancutsem, C., Avitabile, V., Beck, P. S. A., Beuchle, R., Blanc, L., Duveiller, G., Migliavacca, M., Vieilledent, G., Cescatti, A., Achard, F. | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Forests, Evergreen Vegetation, Deciduous Vegetation, Shrubland/Scrub, Biomass, Grasslands, LIDAR WAVEFORM | |
| Genetic Programming Guided Mapping of Forest Canopy Height by Combining | Wu, Zhenjiang, Yao, Fengmei, Zhang, Jiahua, Ma, Enhua, Yao, Liping, Dong, Zhaowei | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Improving global digital elevation models using space-borne GEDI and ICESat-2 LiDAR altimetry data | Narin, Omer Gokberk, Abdikan, Saygin, Gullu, Mevlut, Lindenbergh, Roderik, Balik Sanli, Fusun, Yilmaz, Ibrahim | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Improving mean water lake surface elevation estimates using dense lidar measurements from the GEDI satellite mission | Frappart, Frederic, Tong Minh, Dinh Ho, Baghdadi, Nicolas, Cretaux, Jean-Francois, Fayad, Ibrahim, Berge-Nguyen, Muriel | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM | |
| Incorporating of spatial effects in forest canopy height mapping using airborne, spaceborne lidar and spatial continuous remote sensing data | Min, Wankun, Chen, Yumin, Huang, Wenli, Wilson, John P., Tang, Hao, Guo, Meiyu, Xu, Rui | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Definition criteria determine the success of old-growth mapping | Bruening, Jamis M., Dubayah, Ralph O., Pederson, Neil, Poulter, Benjamin, Calle, Leonardo | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations | Liu, Xiao, Neigh, Christopher S.R., Pardini, Matteo, Forkel, Matthias | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Forests, Evergreen Vegetation, Deciduous Vegetation, Shrubland/Scrub, Biomass, Grasslands, LIDAR WAVEFORM, Plant Characteristics, Terrain Elevation, Vegetation Species, Forest Composition/Vegetation Structure, Dominant Species |
Variables
The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.
| Name Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| /BEAM0000/geolocation/quality_flag_a2 | Flag simpilfying selection of most useful data | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/geolocation/quality_flag_a3 | Flag simpilfying selection of most useful data | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/geolocation/quality_flag_a4 | Flag simpilfying selection of most useful data | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/geolocation/quality_flag_a5 | Flag simpilfying selection of most useful data | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/geolocation/quality_flag_a6 | Flag simpilfying selection of most useful data | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/geolocation/rh_a1 | Relative height metrics at 1 % intervals using algorithm N (in cm) | cm | int16 | N/A | -213 to 213 | N/A | N/A |
| /BEAM0000/geolocation/rh_a2 | Relative height metrics at 1 % intervals using algorithm N (in cm) | cm | int16 | N/A | -213 to 213 | N/A | N/A |
| /BEAM0000/geolocation/rh_a3 | Relative height metrics at 1 % intervals using algorithm N (in cm) | cm | int16 | N/A | -213 to 213 | N/A | N/A |
| /BEAM0000/geolocation/rh_a4 | Relative height metrics at 1 % intervals using algorithm N (in cm) | cm | int16 | N/A | -213 to 213 | N/A | N/A |
| /BEAM0000/geolocation/rh_a5 | Relative height metrics at 1 % intervals using algorithm N (in cm) | cm | int16 | N/A | -213 to 213 | N/A | N/A |
| /BEAM0000/geolocation/rh_a6 | Relative height metrics at 1 % intervals using algorithm N (in cm) | cm | int16 | N/A | -213 to 213 | N/A | N/A |
| /BEAM0000/geolocation/sensitivity_a1 | Maxmimum canopy cover, using algorithmN, that can be penetrated considering the SNR of the waveform | N/A | float32 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/geolocation/sensitivity_a2 | Maxmimum canopy cover, using algorithmN, that can be penetrated considering the SNR of the waveform | N/A | float32 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/geolocation/sensitivity_a3 | Maxmimum canopy cover, using algorithmN, that can be penetrated considering the SNR of the waveform | N/A | float32 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/geolocation/sensitivity_a4 | Maxmimum canopy cover, using algorithmN, that can be penetrated considering the SNR of the waveform | N/A | float32 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/geolocation/sensitivity_a5 | Maxmimum canopy cover, using algorithmN, that can be penetrated considering the SNR of the waveform | N/A | float32 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/geolocation/sensitivity_a6 | Maxmimum canopy cover, using algorithmN, that can be penetrated considering the SNR of the waveform | N/A | float32 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/geolocation/shot_number | Shot number | N/A | uint64 | N/A | N/A | N/A | N/A |
| /BEAM0000/geolocation/stale_return_flag | Flag from digitizer indicating the real-time pulse detection algorithm did not detect a return signal above its detection threshold within the entire 10 km search window. The pulse location of the previous shot was used to select the telemetered waveform. | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/land_cover_data/landsat_treecover | Tree cover in the year 2010, defined as canopy closure for all vegetation taller than 5m in height (Hansen et al.). Encoded as a percentage per output grid cell. | percent | float64 | -9999 | N/A | N/A | N/A |
| /BEAM0000/land_cover_data/landsat_water_persistence | The percent UMD GLAD Landsat observations with classified surface water between 2018 and 2019. Values > 80 usually represent permanent water, while values < 10 represent permanent land. | N/A | uint8 | N/A | 0 to 100 | N/A | N/A |
| /BEAM0000/land_cover_data/leaf_off_doy | GEDI 1 km EASE 2.0 grid leaf-off start day-of-year derived from the NPP VIIRS Global Land Surface Phenology Product. | N/A | int16 | 32767 | 1 to 365 | N/A | N/A |
| /BEAM0000/land_cover_data/leaf_off_flag | GEDI 1 km EASE 2.0 grid flag derived from leaf_off_doy, leaf_on_doy and pft_class, indicating if the observation was recorded during leaf-off conditions in deciduous forests and woodlands. 1 = leaf-off and 0 = leaf-on. | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/land_cover_data/leaf_on_cycle | Flag that indicates the vegetation growing cycle for leaf-on observations. Values are 0 (leaf-off conditions), 1 (cycle 1) or 2 (cycle 2). | N/A | uint8 | 255 | 1 to 2 | N/A | N/A |
| /BEAM0000/land_cover_data/leaf_on_doy | GEDI 1 km EASE 2.0 grid leaf-on start day-of-year derived from the NPP VIIRS Global Land Surface Phenology Product. | N/A | int16 | 32767 | 1 to 365 | N/A | N/A |
| /BEAM0000/land_cover_data/modis_nonvegetated | Percent non-vegetated from MODIS data. Interpolated at latitude_bin0 and longitude_bin0. doi:10.5067/MODIS/MOD44B.006 | percent | float64 | -9999 | N/A | N/A | N/A |
| /BEAM0000/land_cover_data/modis_nonvegetated_sd | Percent non-vegetated standard deviation from MODIS data. Interpolated at latitude_bin0 and longitude_bin0. doi:10.5067/MODIS/MOD44B.006 | percent | float64 | -9999 | N/A | N/A | N/A |
| /BEAM0000/land_cover_data/modis_treecover | Percent tree cover from MODIS data. Interpolated at latitude_bin0 and longitude_bin0. doi:10.5067/MODIS/MOD44B.006 | percent | float64 | -9999 | N/A | N/A | N/A |
| /BEAM0000/land_cover_data/modis_treecover_sd | Percent tree cover standard deviation from MODIS data. Interpolated at latitude_bin0 and longitude_bin0. doi:10.5067/MODIS/MOD44B.006 | percent | float64 | -9999 | N/A | N/A | N/A |
| /BEAM0000/land_cover_data/pft_class | GEDI 1 km EASE 2.0 grid Plant Functional Type (PFT) derived from the MODIS MCD12Q1v006 Product. Values follow the Land Cover Type 5 Classification scheme. | N/A | uint8 | N/A | 0 to 11 | N/A | N/A |
| /BEAM0000/land_cover_data/region_class | GEDI 1 km EASE 2.0 grid world continental regions (0: Water, 1: Europe, 2: North Asia, 5: South Asia, 3: Australasia, 4: Africa, 6: South America, 7: North America). | N/A | uint8 | N/A | 0 to 7 | N/A | N/A |
| /BEAM0000/land_cover_data/urban_focal_window_size | The focal window size used to calculate urban_proportion. Values are 3 (3x3 pixel window size) or 5 (5x5 pixel window size). | N/A | uint8 | N/A | 3 to 5 | N/A | N/A |
| /BEAM0000/land_cover_data/urban_proportion | The percentage proportion of land area within a focal area surrounding each shot that is urban land cover. Urban land cover is derived from the DLR 12 m resolution TanDEM-X Global Urban Footprint Product. | N/A | uint8 | N/A | 0 to 100 | N/A | N/A |
| /BEAM0000/latitude_bin0_error | Error in latitude of bin 0 | degrees | float32 | N/A | N/A | N/A | N/A |
| /BEAM0000/lat_highestreturn | Latitude of highest detected return | degrees | float64 | N/A | -55 to 55 | N/A | N/A |
| /BEAM0000/lat_lowestmode | Latitude of center of lowest mode | degrees | float64 | N/A | -55 to 55 | N/A | N/A |
| /BEAM0000/longitude_bin0_error | Error on longitude_bin0. | degrees | float32 | N/A | N/A | N/A | N/A |
| /BEAM0000/lon_highestreturn | Longitude of highest detected return | degrees | float64 | N/A | -180 to 180 | N/A | N/A |
| /BEAM0000/lon_lowestmode | Longitude of center of lowest mode | degrees | float64 | N/A | -180 to 180 | N/A | N/A |
| /BEAM0000/master_frac | Master time, fractional part. master_int+master_frac is equivalent to /BEAMXXXX/geolocation/delta_time and /BEAMXXXX/geophys_corr/delta_time. | s | float64 | N/A | N/A | N/A | N/A |
| /BEAM0000/master_int | Master time, integer part. Seconds since master_time_epoch. master_int+master_frac is equivalent to /BEAMXXXX/geolocation/delta_time and /BEAMXXXX/geophys_corr/delta_time. | s | uint32 | N/A | N/A | N/A | N/A |
| /BEAM0000/mean_sea_surface | Mean sea surface height above the WGS84 ellipsoid, includes the geoid. Interpolated at latitude_bin0 and longitude_bin0 from DTU15. | m | float32 | N/A | N/A | N/A | N/A |
| /BEAM0000/num_detectedmodes | Number of detected modes in rxwaveform | N/A | uint8 | N/A | 0 to 20 | N/A | N/A |
| /BEAM0000/quality_flag | Flag simpilfying selection of most useful data | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rh | Relative height metrics at 1 % interval | m | float32 | N/A | -213 to 213 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/ancillary/mpfit_maxiters | Maximum number of iterations allowed for fitting the Gaussian/extended Gaussian to the rxwaveform | N/A | int64 | N/A | 0 to 100 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/ancillary/mpfit_max_func_evals | Maximum number of function evalutions allowed for fitting the Gaussian/extended Gaussian to the rxwaveform. | N/A | int64 | N/A | 0 to 100 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/ancillary/mpfit_tolerance | Convergence tolerance when fitting the Gaussian/extended Gaussian to the rxwaveform. | N/A | float64 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/ancillary/rx_constraint_gamplitude_lower | Lower allowable limit for the rx Gaussian fit amplitude | counts | float64 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/ancillary/rx_constraint_gamplitude_upper | Upper allowable limit for the rx Gaussian fit amplitude | counts | float64 | N/A | 0 to 4096 | N/A | N/A |