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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

The following changes and improvements were implemented for GEDI Version 2 data products: (1) Reduced granule size from one full ISS orbit (~7.99 GB) to four segments per orbit (~2.00 GB), (2) Modified file name to include segment number and dataset version, (3) Improved geolocation for orbital segments, (4) Added elevation from the SRTM digital elevation model for comparison, (5) Modified the method to predict an optimum algorithm setting group per laser shot, (6) Added additional land cover datasets related to phenology, urban infrastructure, and water persistence, (7) Added selected_mode_flag dataset to root beam group using selected algorithm, and (8) Removed shots when the laser is not firing.

Product Summary

Platforms
ISS
Instruments
GEDI
Spatial Extent

N: 54 S: -54 E: 180 W: -180

Spatial Resolution
25 Meters x 25 Meters
Location
GLOBAL LAND
Coordinate System
CARTESIAN
Granule Spatial Representation
GEODETIC
Temporal Extent
2019-04-04 to 2023-03-16 ,
2024-04-26 to 2025-07-10
Temporal Resolution
Varies
Data Partner
Land Processes Distributed Active Archive Center (LP DAAC) ,
Global Ecosystem Dynamics Investigation, Goddard Space Flight Center, NASA (NASA/GSFC/GEDI)
Concept ID
C2142771958-LPCLOUD
Data State
COMPLETE
Number of Files/Granules
95770
Processing Level
2A
Published
Updated
Science Keywords
Plant Phenology ,
Canopy Characteristics ,
Vegetation Cover ,
Lidar ,
Topography ,
Vegetation Height

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.

Copy Citation

Dubayah, R., Hofton, M., Blair, J., Armston, J., Tang, H., & Luthcke, S. (2021). GEDI L2A Elevation and Height Metrics Data Global Footprint Level V002 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/GEDI/GEDI02_A.002 Date Accessed: 2026-05-17

File Naming Convention

GEDI02_A_2024333190440_O33777_03_T03689_02_004_02_V002.h5

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

Publications Citing This Dataset

Filters
Title Year Author Topic
Integrating PolInSAR and GEDI data with machine learning for forest canopy height predicting in Pongara National Park, Gabon Benhalima, N., Ouarzeddine, M., Souissi, B., Bengusmia, D. Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height
A dataset of forest regrowth in globally key deforestation regions Zang, Jinlong, Qiu, Feng, Zhang, Yongguang, Shang, Rong, Liang, Yunjian Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height
Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables Alvarez, Maria Paula, Bellis, Laura Marisa, Arcamone, Julieta Rocio, Silvetti, Luna Emilce, Gavier-Pizarro, Gregorio 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
China's annual forest age dataset at a 30 m spatial resolution from 1986 Shang, Rong, Lin, Xudong, Chen, Jing M., Liang, Yunjian, Fang, Keyan, Xu, Mingzhu, Yan, Yulin, Ju, Weimin, Yu, Guirui, He, Nianpeng, Xu, Li, Liu, Liangyun, Li, Jing, Li, Wang, Zhai, Jun, Hu, Zhongmin Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height
Changes in GEDI-based measures of forest structure after large Clark, Matthew L., Hakkenberg, Christopher R., Bailey, Tim, Burns, Patrick, Goetz, Scott J. Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Forests, Evergreen Vegetation, Deciduous Vegetation, Shrubland/Scrub, Biomass, Grasslands, LIDAR WAVEFORM
Characterizing dynamics of built-up height in China from 2005 to 2020 Chen, Peimin, Huang, Huabing, Qin, Peng, Liu, Xiangjiang, Wu, Zhenbang, Zhao, Feng, Liu, Chong, Wang, Jie, Li, Zhan, Cheng, Xiao, Gong, Peng Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height
Multi-source remote sensing for large-scale biomass estimation in Contreras, Francisco, Cayuela, Maria L., Sanchez-Monedero, Miguel A., Perez-Cutillas, Pedro RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Reflectance
Mapping large-scale pantropical forest canopy height by integrating GEDI Qi, Wenlu, Armston, John, Choi, Changhyun, Stovall, Atticus, Saarela, Svetlana, Pardini, Matteo, Fatoyinbo, Lola, Papathanassiou, Konstantinos, Pascual, Adrian, Dubayah, Ralph Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM, Terrestrial Ecosystems, Biomass, Glacier Elevation/Ice Sheet Elevation, Sea Ice Elevation, Terrain Elevation
A geostatistical approach to enhancing national forest biomass Hunka, Neha, May, Paul, Babcock, Chad, de la Rosa, Jose Armando Alanis, de los Angeles Soriano-Luna, Maria, Saucedo, Rafael Mayorga, Armston, John, Santoro, Maurizio, Suarez, Daniela Requena, Herold, Martin, Malaga, Natalia, Healey, Sean P., Kennedy, Robert E., Hudak, Andrew T., Duncanson, Laura Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Forests, Evergreen Vegetation, Shrubland/Scrub, Grasslands, Deciduous Vegetation, Biomass, LIDAR WAVEFORM
A hybrid neural network for mangrove mapping considering tide states Ye, Longjie, Weng, Qihao Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height
A dual-pathway framework for mapping forest age in complex mining landscapes by multi-source remote sensing data and tree growth patterns Ma, Tianyue, Li, Jing, Smith, Andy, Yan, Xingguang, Su, Yiting, Huo, Jiangrun, Li, Yanan, Chen, Dan, Yu, Haixia Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height
A multi-sensor approach allows confident mapping of forest canopy fuel Aragoneses, Elena, Garcia, Mariano, Tang, Hao, Chuvieco, Emilio Canopy Characteristics, Evergreen Vegetation, Crown, Deciduous Vegetation, Leaf Characteristics, Vegetation Cover, Land Use/Land Cover Classification, Photosynthesis, Primary Production, Vegetation Productivity, Plant Phenology, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM
gediDB: A toolbox for processing and providing Global Ecosystem Dynamics Investigation (GEDI) L2A-B and L4A-C data Besnard, Simon, Dombrowski, Felix, Holcomb, Amelia Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, Forests, Evergreen Vegetation, Deciduous Vegetation, Shrubland/Scrub, Biomass, Grasslands, LIDAR WAVEFORM
Forest canopy cover estimation with machine learning using GEDI and Seyrek, Eren Can, Narin, Omer Gokberk, Uysal, Murat Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height
Assessing SWOT interferometric SAR altimetry for inland water monitoring: insights from Lake Leman Bazzi, Henri, Baghdadi, Nicolas, Ngo, Yen-Nhi, Normandin, Cassandra, Frappart, Frederic, Cazals, Cecile Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM, Rivers/Streams, Surface Water Processes/Measurements, Lakes/Reservoirs
Modeling structural attributes of Hyrcanian forests using spaceborne LiDAR, machine learning algorithms, and a hierarchical field data collection approach Sohrabi, Hormoz, Akhavan, Reza Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation
Quantifying forest stocking changes in Sundarbans mangrove using remote sensing data Ali, Yaqub, Rahman, M. Mahmudur Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height
Reconstruction of understory terrain based on machine learning combined Xu, Weifeng, Li, Jun, Peng, Dailiang, Wen, Di Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height
Spatial Characterization of Woody Species Diversity in Tropical Savannas Rex, Franciel Eduardo, Silva, Carlos Alberto, Broadbent, Eben North, Dalla Corte, Ana Paula, Leite, Rodrigo, Hudak, Andrew, Hamamura, Caio, Latifi, Hooman, Xiao, Jingfeng, Atkins, Jeff W., Amaral, Cibele, Cunha Neto, Ernandes Macedo da, Cardil, Adrian, Almeyda Zambrano, Angelica M. Almeyda, Liesenberg, Veraldo, Liang, Jingjing, De Almeida, Danilo Roberti Alves, Klauberg, Carine Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation
State of the art and for remote sensing monitoring of carbon dynamics in African tropical forests Bossy, Thomas, Ciais, Philippe, Renaudineau, Solene, Wan, Liang, Ygorra, Bertrand, Adam, Elhadi, Barbier, Nicolas, Bauters, Marijn, Delbart, Nicolas, Frappart, Frederic, Gara, Tawanda Winmore, Hamunyela, Eliakim, Ifo, Suspense Averti, Jaffrain, Gabriel, Maisongrande, Philippe, Mugabowindekwe, Maurice, Mugiraneza, Theodomir, Normandin, Cassandra, Obame, Conan Vassily, Peaucelle, Marc, Pinet, Camille, Ploton, Pierre, Sagang, Le Bienfaiteur, Schwartz, Martin, Sollier, Valentine, Sonke, Bonaventure, Tresson, Paul, De Truchis, Aurelien, Vo Quang, An, Wigneron, Jean-Pierre Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Terrestrial Ecosystems, LIDAR WAVEFORM, Biomass
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
Satellite-based mapping of annual canopy height and aboveground biomass in African dense forests Wan, Liang, Ciais, Philippe, de Truchis, Aurelien, Sean, Ewan, Fischer, Fabian Jorg, Purnell, David, Belouze, Gabriel, Fayad, Ibrahim, Schwartz, Martin, Xu, Yidi, Su, Yang, Rejou-Mechain, Maxime, Barbier, Nicolas, Tresson, Paul, Bastin, Jean-Francois, Bogaert, Jan, Vander Linden, Arthur, Plumacker, Antoine, Angoboy Ilondea, Bhely, Assumani, Dieu-Merci, de Haulleville, Thales, Sagang, Le Bienfaiteur, Durieux, Laurent, Ryu, Youngryel, Yang, Tackang, Obame, Conan Vassily, Bossy, Thomas, Frappart, Frederic, Peaucelle, Marc, Wigneron, Jean-Pierre, Chave, Jerome, Cuni-Sanchez, Aida, Hubau, Wannes, Verbeeck, Hans, Boeckx, Pascal, Makana, Jean-Remy, Ewango, Corneille, Kearsley, Elizabeth, Sonke, Bonaventure, Libalah, Moses, Ploton, Pierre Terrestrial Ecosystems, LIDAR WAVEFORM, Biomass, Forests, Plant Characteristics, Canopy Characteristics, Terrain Elevation, Carbon, Plant Phenology, Vegetation Cover, Lidar, Topography, Vegetation Height
Spaceborne lidar observations reveal impacts of inundation on coastal Powell, Elisabeth, Dubayah, Ralph, Tully, Kate, Laurent, Kari St, Duncanson, Laura, Fatoyinbo, Lola Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, Terrestrial Ecosystems, LIDAR WAVEFORM, Biomass
Using airborne LiDAR and enhanced-geolocated GEDI metrics to map structural traits over a Mediterranean forest Cardenas-Martinez, Aaron, Pascual, Adrian, Guisado-Pintado, Emilia, Rodriguez-Galiano, Victor Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, Terrestrial Ecosystems, LIDAR WAVEFORM, Biomass
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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 Description Units Data Type Fill Value Valid Range Scale Factor Offset
/BEAM0000/geolocation/lat_highestreturn_a1 Latitude of the highest return detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_highestreturn_a2 Latitude of the highest return detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_highestreturn_a3 Latitude of the highest return detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_highestreturn_a4 Latitude of the highest return detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_highestreturn_a5 Latitude of the highest return detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_highestreturn_a6 Latitude of the highest return detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_lowestmode_a1 Latitude of the center of the lowest mode detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_lowestmode_a2 Latitude of the center of the lowest mode detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_lowestmode_a3 Latitude of the center of the lowest mode detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_lowestmode_a4 Latitude of the center of the lowest mode detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_lowestmode_a5 Latitude of the center of the lowest mode detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_lowestmode_a6 Latitude of the center of the lowest mode detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_lowestreturn_a1 Latitude of the lowest return detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_lowestreturn_a2 Latitude of the lowest return detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_lowestreturn_a3 Latitude of the lowest return detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_lowestreturn_a4 Latitude of the lowest return detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_lowestreturn_a5 Latitude of the lowest return detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/lat_lowestreturn_a6 Latitude of the lowest return detected using algorithm N degrees float64 N/A -55 to 55 N/A N/A
/BEAM0000/geolocation/longitude_1gfit Longitude corresponding to the center of a single gaussian fit to the waveform degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lons_allmodes_a1 Longitudes of all modes detected using algorithmN degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lons_allmodes_a2 Longitudes of all modes detected using algorithmN degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lons_allmodes_a3 Longitudes of all modes detected using algorithmN degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lons_allmodes_a4 Longitudes of all modes detected using algorithmN degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lons_allmodes_a5 Longitudes of all modes detected using algorithmN degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lons_allmodes_a6 Longitudes of all modes detected using algorithmN degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_highestreturn_a1 Longitude of the highest return detected using algorithm N degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_highestreturn_a2 Longitude of the highest return detected using algorithm N degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_highestreturn_a3 Longitude of the highest return detected using algorithm N degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_highestreturn_a4 Longitude of the highest return detected using algorithm N degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_highestreturn_a5 Longitude of the highest return detected using algorithm N degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_highestreturn_a6 Longitude of the highest return detected using algorithm N degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_lowestmode_a1 Longitude of the center of lowest mode detected using algorithm N degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_lowestmode_a2 Longitude of the center of lowest mode detected using algorithm N degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_lowestmode_a3 Longitude of the center of lowest mode detected using algorithm N degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_lowestmode_a4 Longitude of the center of lowest mode detected using algorithm N degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_lowestmode_a5 Longitude of the center of lowest mode detected using algorithm N degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_lowestmode_a6 Longitude of the center of lowest mode detected using algorithm N degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_lowestreturn_a1 Longitude of lowest return detected using algorithmN degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_lowestreturn_a2 Longitude of lowest return detected using algorithmN degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_lowestreturn_a3 Longitude of lowest return detected using algorithmN degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_lowestreturn_a4 Longitude of lowest return detected using algorithmN degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_lowestreturn_a5 Longitude of lowest return detected using algorithmN degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/lon_lowestreturn_a6 Longitude of lowest return detected using algorithmN degrees float64 N/A -180 to 180 N/A N/A
/BEAM0000/geolocation/num_detectedmodes_a1 Number of detected modes detected using algorithm N N/A uint8 N/A 0 to 20 N/A N/A
/BEAM0000/geolocation/num_detectedmodes_a2 Number of detected modes detected using algorithm N N/A uint8 N/A 0 to 20 N/A N/A
/BEAM0000/geolocation/num_detectedmodes_a3 Number of detected modes detected using algorithm N N/A uint8 N/A 0 to 20 N/A N/A
/BEAM0000/geolocation/num_detectedmodes_a4 Number of detected modes detected using algorithm N N/A uint8 N/A 0 to 20 N/A N/A
/BEAM0000/geolocation/num_detectedmodes_a5 Number of detected modes detected using algorithm N N/A uint8 N/A 0 to 20 N/A N/A
/BEAM0000/geolocation/num_detectedmodes_a6 Number of detected modes detected using algorithm N N/A uint8 N/A 0 to 20 N/A N/A
/BEAM0000/geolocation/quality_flag_a1 Flag simpilfying selection of most useful data N/A uint8 N/A 0 to 1 N/A N/A
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