--- - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: The focus of this study was to quantify the effects of foliage removal by cattle on plant net primary productivity (NPP). The Vegetation Biomass, Production and Consumption at Selected Sites Data Set contains mean values and their variances. During the growing season of 1987, portable cattle exclosures were used to quantify above-ground plant biomass dynamics at each of four sites. All sites had been grazed each year and burned frequently during the preceding 10 years. Biomass was measured inside portable exclosures, outside exclosures (in unprotected vegetation), and inside permanent exclosures. Exclosures were moved to previously unsampled locations within a distance of 10 m after samples were obtained, and these remained in place until the next sampling date.' description_attribution: http://daac.ornl.gov//FIFE/guides/Plant_Biomass_Production_Consump.html doi: 10.3334/ORNLDAAC/69 end_time: 1987-10-13T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-69.yaml identifier: nasa-ornldaac-69 lat_max: 39.12 lat_min: 38.98 lon_max: -96.47 lon_min: -96.54 name: Plant Biomass/Production/Consump. (FIFE) native_id: FIFE_PLANTPRO processing_level: ~ publication_year: ~ release_dt: 1994-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1987-05-18T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-69 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=69 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "ABSTRACT: This data set is a 1-kilometer resolution land cover map for the land area of the Primor'ye and Southern Khabarovsk Regions, in the Russian Far East, based on 1990 NOAA AVHRR data. Labeling of land cover classes depended upon the Russian 1990 Forest Cover Map (Garsia, 1990), the analyst's experience with AVHRR data, and Russian data sources. There are eight classes distinguished in this dataset, of which 5 are forest cover classes.The objective of this work was to create a 1-km resolution land cover map of the region of the Far Eastern Siberia based on NOAA AVHRR data which might be used by World Wildlife Fund researchers to aid in the definition of remaining habitats and range for threatened animal species (Stone and Schlesinger, 1996)." description_attribution: http://daac.ornl.gov//RLC/guides/RLC_AVHRR_1_km.html doi: 10.3334/ORNLDAAC/690 end_time: 1990-08-17T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-690.yaml identifier: nasa-ornldaac-690 lat_max: 71 lat_min: 23.21 lon_max: 180 lon_min: 25 name: 'RLC AVHRR-Derived Land Cover, Former Soviet Union, Far East, 1-km, 1990' native_id: rlc_avhrr_1km processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1990-05-15T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-690 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=690 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: This data set is a 1:2.5 million scale forest cover map for the land area of the Former Soviet Union that was completed in 1990 (Garsia 1990). There are forty-five classes distinguished in this data set, of which 38 are forest cover classes. The purpose of this map was to create a generalized and up-to-date map of forest cover for the USSR. This map should not be viewed as a detailed forest cover map but more like an economic forestry map. The most important tree species of a region are highlighted rather than the dominant trees species or tree cover. Very few tree species are defined. In many cases, of course, the dominant and the most important trees species are the same.' description_attribution: http://daac.ornl.gov//RLC/guides/RLC_forest90.html doi: 10.3334/ORNLDAAC/691 end_time: 1990-12-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-691.yaml identifier: nasa-ornldaac-691 lat_max: 71 lat_min: 23.21 lon_max: 180 lon_min: 25 name: 'RLC Forest Cover Map of the Former Soviet Union, 1990' native_id: rlc_forest_map_1990 processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1990-01-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-691 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=691 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: This data set is a 1:15 million scale forest cover map for the land area of the Former Soviet Union. Twenty-two land cover classes are distinguished, of which 20 are forest cover classes. The source data were acquired by map digitization from the Atlas of Forests of the USSR (Anon. 1973) which was likely based on forestry data from the 1940s, 1950s and 1960s.' description_attribution: http://daac.ornl.gov//RLC/guides/RLC_forest_cover_73.html doi: 10.3334/ORNLDAAC/692 end_time: 1973-12-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-692.yaml identifier: nasa-ornldaac-692 lat_max: 75 lat_min: 35.1659 lon_max: 170 lon_min: 19.8165 name: 'RLC Forest Cover of the Former Soviet Union, 1973' native_id: rlc_forest_map_1973 processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1973-01-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-692 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=692 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: This dataset is a 1:2 million scale forest cover map for the land area of the Krasnoyarsk Region, Russia. Thirty-two land cover classes are distinguished. These data were digitized from maps of the Atlas of Forests of the USSR (Anon. 1973). This map should not be strictly viewed as a map of actual forest cover, but rather as a map of dominant tree species. Very few tree species are defined, and generally, each polygon and color has only one tree species assigned to it.' description_attribution: http://daac.ornl.gov//RLC/guides/RLC_forest_krasnoyarsk_73.html doi: 10.3334/ORNLDAAC/693 end_time: 1973-12-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-693.yaml identifier: nasa-ornldaac-693 lat_max: 56.5 lat_min: 56.5 lon_max: 104.5 lon_min: 104.5 name: 'RLC Forest Cover of the Krasnoyarsk Region, Russia, 1973' native_id: rlc_forest_cover processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1973-01-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-693 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=693 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "ABSTRACT: This data set is made up of images of forest fires in Russia from NOAA's Operational Significant Event Imagery (OSEI) archive (http://www.osei.noaa.gov) for the 1998 and 1999 seasons. OSEI fire products include multichannel color composite imagery of wildfire and controlled burn events. Products in this event group show fire, smoke, and hotspots (FSMHS) from the fires." description_attribution: http://daac.ornl.gov//RLC/guides/RLC_forest_fire_images.html doi: 10.3334/ORNLDAAC/694 end_time: 1999-08-03T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-694.yaml identifier: nasa-ornldaac-694 lat_max: 71 lat_min: 23.21 lon_max: 180 lon_min: 25 name: 'RLC Forest Fire Images in Russia, 1998-1999' native_id: rlc_forest_fire_img processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1998-08-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-694 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=694 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: This dataset is derived from Russian forest fire imagery from the National Forest Fire Center of Russia archive that was collected by the Center of Remote Sensing, Institute of Solar Terrestrial Physics, Irkutsk, Russia for the 1998 and 1999 fire seasons. The data are vector (point) maps of forest fire locations (1998 and 1999) in ArcView shapefile format.' description_attribution: http://daac.ornl.gov//RLC/guides/RLC_east_forest_fires.html doi: 10.3334/ORNLDAAC/695 end_time: 1999-09-30T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-695.yaml identifier: nasa-ornldaac-695 lat_max: 71 lat_min: 23.21 lon_max: 180 lon_min: 25 name: 'RLC Forest Fire Locations in Eastern Russia, 1998-1999' native_id: rlc_forest_fires processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1998-04-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-695 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=695 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: This dataset is a 1:15 million scale map of forest stand carbon for the land area of Russia (Stone et al., 2000). The objective was to create a first approximation of the forest stand carbon reserves of Russia. Data include continuous estimates of forest stand carbon in units of metric tons/ha of carbon (C) and categorized data depicting rages of forest stand carbon. The resulting maps show forest stand C by region in a spatially explicit form. It is the first map of its type for Russia of which we are aware. The mapped C represents 96% of the total of 26.1 Pg forest tree stand C described by Alexeyev and Birdsey (1994) and Alexeyev et al. (1995). Of the remaining 4%, nearly half was due to bushes, which were assumed not to be mapped in the 1973 forest cover map.The source data for the forest stand carbon map were acquired by map digitization from the Atlas of Forests for the Soviet Union (State Committee on Forests, 1973) and spatial application and arithmetic manipulation of carbon storage data from Alexeyev and Birdsey (1998).' description_attribution: http://daac.ornl.gov//RLC/guides/RLC_forest_carbon_73.html doi: 10.3334/ORNLDAAC/696 end_time: 1973-12-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-696.yaml identifier: nasa-ornldaac-696 lat_max: 75 lat_min: 35.1659 lon_max: 170 lon_min: 19.8165 name: RLC Forest Stand Carbon Map of Russia native_id: rlc_forest_carbon processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1973-01-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-696 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=696 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: This data set is the Former Soviet Union (FSU) portion of the Generalized World Forest Map (WCMC, 1998), a 1-kilometer resolution generalized forest cover map for the land area of the Former Soviet Union. There are five forest classes in the original global generalized map. Only two of those classes were distinguished in the geographical portion comprising the FSU.' description_attribution: http://daac.ornl.gov//RLC/guides/RLC_forestmap98.html doi: 10.3334/ORNLDAAC/697 end_time: 1998-12-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-697.yaml identifier: nasa-ornldaac-697 lat_max: 71 lat_min: 23.21 lon_max: 180 lon_min: 25 name: 'RLC Generalized Forest Map of the Former Soviet Union, 1-km' native_id: rlc_world_forest_map processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1998-01-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-697 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=697 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: This data set consists of roads, drainage, railroads, utilities, and population center information in readily usable vector format for the land area of the Former Soviet Union. The purpose of this dataset was to create a completely intact vector layer which could be readily used to aid in mapping efforts for the area of the FSU. These five vector data layers were assembled from the Digital Chart of the World (DCW), 1993. Individual record attributes were stored for population centers only. Vector maps for the FSU are in ArcView shapefile format.' description_attribution: http://daac.ornl.gov//RLC/guides/RLC_vector_data.html doi: 10.3334/ORNLDAAC/698 end_time: 1993-12-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-698.yaml identifier: nasa-ornldaac-698 lat_max: 71 lat_min: 23.21 lon_max: 180 lon_min: 25 name: 'RLC Selected Infrastructure Data for the Former Soviet Union, 1993' native_id: rlc_infra_1993 processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1993-01-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-698 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=698 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: This data set of state and regional boundaries was derived from the 1:3 million scale administrative boundaries (ESRI, 1998) for the land area of the Former Soviet Union. There are 162 administrative regions distinguished in this data set. The vector map of state and regional boundaries for the FSU is in ArcView shapefile format.' description_attribution: http://daac.ornl.gov//RLC/guides/RLC_admin_bound.html doi: 10.3334/ORNLDAAC/699 end_time: 1999-08-03T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-699.yaml identifier: nasa-ornldaac-699 lat_max: 71 lat_min: 23.21 lon_max: 180 lon_min: 25 name: RLC State and Regional Boundaries for the Former Soviet Union native_id: rlc_boundaries processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1998-08-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-699 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=699 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: The University of Wyoming (UW) King Air atmospheric boundary layer measurement missions were flown in 1987 during IFCs 3 and 4. This Boundary Layer Fluxes data set contains parameters that describe the environment in which the flux data were collected and the flux data itself . The fluctuations in all variables were calculated with three different methods (the arithmetic means removed, the linear trends removed, or filtered with a high-pass recursive filter) prior to the eddy correlation calculations. This data set contains the data that has been filtered using a high-pass recursive filter. All the flux measurements were obtained with the eddy-correlation method, wherein the aircraft is equipped with an inertial platform, accelerometers, and a gust probe for measurement of earth-relative gusts in the x, y, and z directions. Gusts in these dimensions are then correlated with each other for momentum fluxes and with fluctuations in other variables to obtain the various scalar fluxes, such as temperature (for sensible heat flux) and water vapor mixing ratio (for latent heat flux). The summary of data calculated from each aircraft pass includes various statistics, correlations, and fluxes calculated after the time series for each variable with the linear trends removed.' description_attribution: http://daac.ornl.gov//FIFE/guides/air_flux_filt_wy.html doi: 10.3334/ORNLDAAC/7 end_time: 1989-10-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-7.yaml identifier: nasa-ornldaac-7 lat_max: 40 lat_min: 37 lon_max: -95 lon_min: -102 name: 'Aircraft Flux-Filtered: U of Wy. (FIFE)' native_id: FIFE_AF_FLT_K processing_level: ~ publication_year: ~ release_dt: 1994-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1987-08-11T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-7 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=7 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: The Surface Radiant Temperature Measured with a Helicopter-borne Infrared Thermometer Data Set were collected for six days during July and August of 1989 to provide the radiant temperature of the FIFE sites and as a check of the thermal band on the MMR. The average and standard deviation of radiant temperature were measured with an Everest infrared thermometer. The Everest Series 4000 Infrared Thermometer (IRT) was mounted on the NASA Bell UH-1B helicopter in conjunction with the Barnes Multiband Modular Radiometer (MMR) and the Spectron Engineering SE590 Spectroradiometer for the 1989 field campaign. The IRT collected radiant temperature data as the helicopter hovered over individual sites within the FIFE study area.' description_attribution: http://daac.ornl.gov//FIFE/guides/Radiant_Temp_Helicopter_Data.html doi: 10.3334/ORNLDAAC/70 end_time: 1989-08-11T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-70.yaml identifier: nasa-ornldaac-70 lat_max: 39.12 lat_min: 38.98 lon_max: -96.45 lon_min: -96.61 name: Radiant Temp. Helicopter Data (FIFE) native_id: FIFE_IRT_HELO processing_level: ~ publication_year: ~ release_dt: 1994-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1989-07-28T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-70 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=70 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: This dataset is a 1:4 million scale vegetation map for the land area of the Former Soviet Union. Three hundred seventy-three cover classes are distinguished, of which nearly 145 are forest cover-related classes. Stone and Schlesinger (1993) digitized the map Vegetation of the Soviet Union, 1990 (Institute of Geography, 1990).' description_attribution: http://daac.ornl.gov//RLC/guides/RLC_veg_cover_1990.html doi: 10.3334/ORNLDAAC/700 end_time: 1973-12-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-700.yaml identifier: nasa-ornldaac-700 lat_max: 75 lat_min: 35.1659 lon_max: 170 lon_min: 19.8165 name: 'RLC Vegetative Cover of the Former Soviet Union, 1990' native_id: rlc_veg_cover processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1973-01-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-700 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=700 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: The data set consists of a subset of the ISRIC-WISE global data set of derived soil properties for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 to 30 degrees W, latitude 25 degrees S to 10 degrees N).The World Inventory of Soil Emission Potentials (WISE) database currently contains data for over 4300 soil profiles collected mostly between 1950 and 1995. This database has been used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World (FAO-UNESCO, 1974). These data sets were then linked to a 1/2 degree longitude by 1/2 degree latitude version of the edited and digital Soil Map of the World (FAO, 1995) to generate GIS raster image files for the following variables:Total available water capacity (mm water per 1 m soil depth)Soil organic carbon density (kg C/m**2 for 0-30 cm depth range)Soil organic carbon density (kg C/m**2 for 0-100 cm depth range)Soil carbonate carbon density (kg C/m**2 for 0-100 cm depth range)Soil pH (0-30 cm depth range)Soil pH (30-100 cm depth range)LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. LBA was a cooperative international research initiative led by Brazil and NASA was a lead sponsor for several experiments. More information about LBA and links to other LBA project sites can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.' description_attribution: http://daac.ornl.gov//LBA/guides/lba_isric.html doi: 10.3334/ORNLDAAC/701 end_time: 1995-12-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-701.yaml identifier: nasa-ornldaac-701 lat_max: 10 lat_min: -25 lon_max: -30 lon_min: -85 name: 'LBA Regional Derived Soil Properties, 0.5-Deg (ISRIC-WISE)' native_id: lba_isric_wise processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1950-01-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-701 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=701 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "ABSTRACT: This data set consists of a subset of the Global Historical Climatology Network (GHCN) Version 1 database for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 to 30 degrees W, latitude 25 degrees S to 10 degrees N). There are three files available, one each for precipitation, temperature, and pressure data. Within this subset the oldest data date from 1832 and the most recent from 1990.The GHCN V1 database contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which was to produce, maintain and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea-level pressure stations, and 1800 station-pressure stations. Each station has at least 10 years of data; 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara desert, Greenland, and Antarctica. The earliest station data are from 1697; the most recent are from 1990. The database was created from 15 source data sets including:The National Climatic Data Center's (NCDC's) World Weather Records,CAC's Climate Anomaly Monitoring System (CAMS),NCAR's World Monthly Surface Station Climatology,CIRES' (Eischeid/Diaz) Global precipitation data set,P. Jones' Temperature data base for the world, andS. Nicholson's African precipitation database. Quality Control of the GHCN V1 database included visual inspection of graphs of all station time series, tests for precipitation digitized 6 months out of phase, tests for different stations having identical data, and other tests. This detailed analysis has revealed that most stations (95% for temperature and precipitation, 75% for pressure) contain high-quality data. However, gross data-processing errors (e.g., keypunch problems) and discontinuous inhomogeneities (e.g., station relocations and instrumentation changes) do characterize a small number of stations. All major data processing problems have been flagged (or corrected, when possible). Similarly, all major inhomogeneities have been flagged, although no homogeneity corrections were applied.LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. LBA was a cooperative international research initiative led by Brazil and NASA was a lead sponsor for several experiments. More information about LBA and links to other LBA project sites can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html." description_attribution: http://daac.ornl.gov//LBA/guides/lba_ghcn_safari.html doi: 10.3334/ORNLDAAC/702 end_time: 1990-12-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-702.yaml identifier: nasa-ornldaac-702 lat_max: 10 lat_min: -25 lon_max: -30 lon_min: -85 name: 'LBA Regional Global Historical Climatology Network, V. 1, 1832-1990' native_id: lba_ghcn_v1 processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1832-07-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-702 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=702 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: This data set of leaf, stem, and root biomass for various plant taxa was compiled from the primary literature of the 20th century with a significant portion derived from Cannell (1982). Recent allometric additions include measurements made by Niklas and colleagues (Niklas, 2003). This is a unique data set with which to evaluate allometric patterns of standing biomass within and across the broad spectrum of vascular plant species. Despite its importance to ecology, global climate research, and evolutionary and ecological theory, the general principles underlying how plant metabolic production is allocated to above- and below-ground biomass remain unclear. The resulting uncertainty severely limits the accuracy of models for many ecologically and evolutionarily important phenomena across taxonomically diverse communities. Thus, although quantitative assessments of biomass allocation patterns are central to biology, theoretical or empirical assessments of these patterns remain contentious.' description_attribution: http://daac.ornl.gov//VEGETATION/guides/niklas_plant_biomass.html doi: 10.3334/ORNLDAAC/703 end_time: 2003-07-15T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-703.yaml identifier: nasa-ornldaac-703 lat_max: 90 lat_min: -90 lon_max: 180 lon_min: -180 name: Biomass Allocation and Growth Data of Seeded Plants native_id: niklas_biomass processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1922-07-15T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-703 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=703 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: The U.S. Department of Agriculture, Agriculture and Agri-Food Canada, the Russian Academy of Agricultural Sciences, the University of Copenhagen Institute of Geography, the European Soil Bureau, the University of Manchester Institute of Landscape Ecology, MTT Agrifood Research Finland, and the Agricultural Research Institute Iceland have shared data and expertise in order to develop the Northern and Mid Latitude Soil Database (Cryosol Working Group, 2001). This database was the source of data for the current product. The spatial coverage of the Northern and Mid Latitude Soil Database is the polar and mid-latitude regions of the northern hemisphere: Alaska, Canada, Conterminous United States, Eurasia (except Italy), Greenland, Iceland, Kazakstan, Mexico, Mongolia, Italy, and Svalbard. The Northern and Mid Latitude Soil Database represents the proportion (percentage) of polygon encompassed by the dominant soil or nonsoil. Soils include turbels, orthels, histels, histosols, mollisols, vertisols, aridisols, andisols, entisols, spodosols, inceptisols (and hapludolls), alfisols (cryalf and udalf), natric great groups, aqu-suborders, glaciers, and rocklands. Also included are data on the circumpolar distribution of gelisols (turbels, orthels, and histels), and the ice content (low, medium, or high) of circumpolar soil materials (from the International Permafrost Association, 1997). The resulting maps show the dominant soil of the spatial polygon unless the polygon is over 90 percent rock or ice. Data are in the U.S. soil classification system and includes the distribution of soil types (%) within a map unit (polygon). Data are available in ESRI shapefile format and include the same attribute values with the exception of Italy, which does not contain distribution values.' description_attribution: http://daac.ornl.gov//SOILS/guides/mid_latitude_soils.html doi: 10.3334/ORNLDAAC/705 end_time: 2001-12-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-705.yaml identifier: nasa-ornldaac-705 lat_max: 71.4 lat_min: 50.9 lon_max: -129.3 lon_min: -180 name: 'Northern and Mid-Latitude Soil Database, Version 1, R1' native_id: global_soil_mid-lat processing_level: ~ publication_year: ~ release_dt: 2014-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 2001-01-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-705 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=705 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "ABSTRACT: AERONET (AErosol RObotic NETwork) is an optical ground-based aerosol monitoring network and data archive system. AERONET measurements of the column-integrated aerosol optical properties in the southern Africa region were made by sun-sky radiometers at several sites in August-September 2000 as a part of the SAFARI 2000 dry season aircraft campaign.AERONET is supported by NASA's Earth Observing System and expanded by federation with many non-NASA institutions. The network hardware consists of identical automatic sun-sky scanning spectral radiometers owned by national agencies and universities. Data from this collaboration provides globally-distributed near-real-time observations of aerosol spectral optical depths, aerosol size distributions, and precipitable water in diverse aerosol regimes.The AERONET (AErosol RObotic NETwork) program is an inclusive federation of ground-based remote sensing aerosol networks established by AERONET and PHOTON and greatly expanded by AEROCAN (the Canadian sun-photometer network) and other agency, institute and university partners. The goal is to assess aerosol optical properties and validate satellite retrievals of aerosol optical properties. The network imposes standardization of instruments, calibration, and processing. Data from this collaboration provides globally distributed observations of spectral aerosol optical depths, inversion products, and precipitable water in geographically diverse aerosol regimes. Three levels of data are available from the AERONET website: Level 1.0 (unscreened), Level 1.5 (cloud-screened), and Level 2.0 (Cloud-screened and quality-assured). (CAUTION: Data presented in the real time data version is unscreened and may not have final calibration reprocessing.) For each site there is a Principal Investigator (PI), the person responsible for deployment, maintenance and data collection. The PI is entitled to be informed of any use of that site data.NOTICE TO NON-AERONET INVESTIGATORS: To maintain the integrity of the data base and fairness to the individuals who have contributed, use of these data for publication requires an offer of authorship to the AERONET PI(s)." description_attribution: http://daac.ornl.gov//S2K/guides/s2k_aeronet.html doi: 10.3334/ORNLDAAC/706 end_time: 2001-12-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-706.yaml identifier: nasa-ornldaac-706 lat_max: -26.186 lat_min: -26.186 lon_max: 28.029 lon_min: 28.029 name: 'SAFARI 2000 AERONET Ground-based Aerosol Data, Dry Season 2000' native_id: s2k_aeronet processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1999-01-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-706 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=706 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "ABSTRACT: These data relate to a paper (Morisette et al., 2005) that describes the use of high spatial resolution ASTER data to determine the accuracy of the moderate resolution MODIS active fire product. Our main objective was to develop a methodology to use ASTER data for quantitative evaluation of the MODIS active fire product and to apply it to fires in Southern Africa during the 2001 burning season. We utilize 18 ASTER scenes distributed throughout Southern Africa covering the time period 5 August 2001 to 6 October 2001. The MODIS fire product is characterized through the use of logistic regression models to establish a relationship between the binary MODIS fire/no fire product and summary statistics derived from ASTER data over the coincident MODIS pixel. Probabilities of detection are determined as a function of the total number of ASTER fires and Moran's I, a measure of the spatial heterogeneity of fires within the MODIS pixel. The statistical analysis is done for versions 3 and 4 of the MODIS fire detection algorithm. It is shown that the algorithm changes have a positive effect on the fire product accuracy. References:Morisette, J. T., L. Giglio, I. Csiszar, C. O. Justice. 2005. Validation of the MODIS active fire product over Southern Africa with ASTER data. International Journal of Remote Sensing 26: 4239-4264." description_attribution: http://daac.ornl.gov//S2K/guides/s2k_modis_aster_fire.html doi: 10.3334/ORNLDAAC/707 end_time: 2001-10-05T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-707.yaml identifier: nasa-ornldaac-707 lat_max: -11.29 lat_min: -26.83 lon_max: 44.26 lon_min: 16.31 name: 'SAFARI 2000 ASTER and MODIS Fire Data Comparison, Dry Season 2001' native_id: s2k_aster_modis processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 2001-08-05T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-707 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=707 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: Data presented in this database are the result of Hand Held 4-band Hazemeters and do not always satisfy to the high standards of quality associated with Automatic Sunphotometers.SAFARI 2000 data are available for the following local measurement sites: Alene_High_School; Chililabombwe; Forestry_Kaoma; Itezhi_Tezhi_Basic; Kafue_Hydrologic; Kalongola; Kananja_Chilanda; Kangaya_Basic; Kapiri_Basic; Kasalu_Basic_School; Kasapa_Basic_School; Kashinakaji_School;Kisasa_Basic; Kitima_Basic_School; Litoya; Livingstone_Met_Dpt; Lubu_Basic_Middle; Lukulu_basic; Lusaka_Met_HQ; Makotolo_School; Met_Kaoma; Miombe; Misamfu_Research; Mongu; Mwayasunka; Mwinilunga; Nalusanga_School; Ndola_Meteorology; Saluzhinka_Basic; Senanga; Shamputa_School; Shikoswe_Basic; Sichili_Primary; Sikufele_School; Sioma_Basic_School; Sitaka; St_Marys; St_Patrics; and Zambezi_Met_Office.Example of data file format:Site: Alene High School, Lat=-11.173000, Long=24.190000PI: Brent HolbenE-mail: brent@aeronet.gsfc.nasa.govt0: 09:08:2000, 00:00:00 Date, Time, Time_offset (days), AOT_1020, AOT_880, AOT_765, AOT_680, AOT_530, AOT_440, AOT_380, Water(cm), Air_Mass09:08:2000, 15:15:12, 0.635556, -100.000000, 1.621565, -100.000000, 1.606205, 1.528689, -100.000000, 0.234460, 0.000000, 3.99342109:08:2000, 15:17:04, 0.636863, -100.000000, 1.589651, -100.000000, 1.565860, 1.501361, -100.000000, 0.369358, 0.000000, 4.110902' description_attribution: http://daac.ornl.gov//S2K/guides/s2k_hazemeter_zamb.html doi: 10.3334/ORNLDAAC/708 end_time: 2000-09-30T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-708.yaml identifier: nasa-ornldaac-708 lat_max: -7.69 lat_min: -18.7 lon_max: 34.45 lon_min: 21.55 name: 'SAFARI 2000 Atmospheric Aerosol Measurements, Hand-held Hazemeters, Zambia' native_id: s2k_hazemeter processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 2000-06-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-708 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=708 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: This record links to the web page for the Cloud Absorption Radiometer (CAR) data for the SAFARI 2000 project. Twenty-one flights were undertaken over Southern Africa during the study. Flight track maps, browse images, and Convair CV-580 flight logs are available on this web site.The Cloud Absorption Radiometer (CAR) is an airborne multi-wavelength scanning radiometer that can perform several functions including: determining the single scattering albedo of clouds at selected wavelengths in the visible and near-infrared; measuring the angular distribution of scattered radiation; measuring bidirectional reflectance of various surface types; and acquiring imagery of cloud and Earth surface features. The CAR instrument was developed at the NASA Goddard Space Flight Center by Dr. Michael King. The CAR now operates from a position mounted in the improved nose cone on a Convair CV-580. In addition to its traditional starboard viewing mode, the CAR instrument can be operated in zenith viewing, nadir viewing, and bidirectional reflectance distribution function (BRDF) mode; and can be switched between each of these four modes during flight.The CAR has been deployed on a regular basis in field campaigns around the world including deployments to Portugal (Azores), Brazil, Kuwait, the conterminous United States, Alaska, and various countries in southern Africa. During typical research field campaigns, the CAR is flown in concert with an array of cloud microphysics, aerosol, atmospheric chemistry, and general meteorological instruments under the direction of the Department of Atmospheric Sciences at the University of Washington.' description_attribution: http://daac.ornl.gov//S2K/guides/s2k_car_brdf.html doi: 10.3334/ORNLDAAC/709 end_time: 2000-09-16T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-709.yaml identifier: nasa-ornldaac-709 lat_max: 5 lat_min: -35 lon_max: 60 lon_min: 5 name: 'SAFARI 2000 Cloud Absorption Radiometer BRDF, Dry Season 2000' native_id: s2k_car_brdf processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 2000-08-15T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-709 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=709 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: The Surface Temperatures Measured at Multiple Angles Data Set was collected at two locations within the northwest quadrant of the FIFE study area during July and August 1989. The data set contains hemispherical surface temperature, surface temperatures measured at several view zenith angles, and surface temperatures and at-view azimuth increments of 45 degrees. These data were collected using the Everest multiplexed infrared thermometers (IRT) Model 4000 and an Eppley Precision Infrared Radiometer Model PIR. Periodically measurements of the surface emissivity and incoming longwave radiation were also made. The purpose of this study was to characterize bi-directional reflectance factor distributions, estimate surface albedo from bi-directional reflectance factor and radiance data, determine the variability of reflected and emitted fluxes in selected spectral wavebands as a function of topography, vegetative community and management practice, determine the influence of plant water status on surface reflectance factors, and determine sun angle affects on radiation fluxes.' description_attribution: http://daac.ornl.gov//FIFE/guides/Radiant_Temp_Multiangle_Data.html doi: 10.3334/ORNLDAAC/71 end_time: 1989-08-10T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-71.yaml identifier: nasa-ornldaac-71 lat_max: 39.09 lat_min: 39.05 lon_max: -96.54 lon_min: -96.55 name: Radiant Temp. Multiangle Data (FIFE) native_id: FIFE_IRT_MULT processing_level: ~ publication_year: ~ release_dt: 1994-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1989-07-12T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-71 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=71 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: "ABSTRACT: The Cloud and Aerosol Research Group (CARG) of the University of Washington participated in the SAFARI-2000 Dry Season Aircraft campaign with their Convair-580 research aircraft. This campaign covered five countries in southern Africa from 10 August through 18 September, 2000. Various types of measurements were obtained on the thirty-one research flights of the Convair-580 in SAFARI-2000, to study their relationships to simultaneous measurements from satellites (particularly Terra), other research aircraft, and SAFARI-2000 ground-based measurements and activities. The main goals of the University of Washington's Convair-580 research aircraft were to: * Measure the physical and chemical properties of aerosols and trace gases in ambient air, and from various sources, in southern Africa. * Obtain measurements on aerosols, trace gases, clouds, and surface properties for comparisons with simultaneous remote sensing measurements from the NASA ER-2 aircraft and Terra satellite and from SAFARI-2000 ground stations. * Carry out closure studies using in situ and remote sensing measurements made aboard the Convair-580. * Compare aerosol and trace gas measurements aloft at various locations in Southern Africa. * Measure the nature and concentrations of aerosols and trace gases, and their emission factors, in smoke from prescribed fires and non-prescribed fires of biomass in southern Africa. * Measure the spectral albedo and bidirectional reflection distribution function (BRDF) of various surfaces and clouds in southern Africa. * Measure the microstructures of clouds off the Atlantic Coast of southern Africa. * Investigate aerosol-cloud interactions. For a complete detailed guide to the extensive measurements obtained aboard the UW Convair-580 aircraft in support of SAFARI 2000, see the UW Technical Report for the SAFARI 2000 Project [PDF format]. The latest version of this document can be found at the UW SAFARI 2000 Web site [Internet Link], listed in the CARG Publications on SAFARI 2000 section." description_attribution: http://daac.ornl.gov//S2K/guides/s2k_CV580.html doi: 10.3334/ORNLDAAC/710 end_time: 2000-09-18T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-710.yaml identifier: nasa-ornldaac-710 lat_max: -14 lat_min: -26 lon_max: 36 lon_min: 11 name: 'SAFARI 2000 CV-580 Aerosol and Cloud Data, Dry Season 2000 (CARG)' native_id: s2k_cv580 processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 2000-08-10T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-710 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=710 variables: ~ version: ~ vertical_extent: ~ - access_dt: ~ attributes: ~ cite_metadata: ~ data_qualifier: ~ description: 'ABSTRACT: The U.S. Agency for International Development (USAID) Famine Early Warning System (FEWS) has been supporting the production of 10-day Rainfall Estimate (RFE) data for Africa since 1995. The FEWSNET project was established with the goal of reducing the incidence of drought- or flood-induced famine by providing decision makers with timely and accurate information on conditions that may require intervention. RFE data for continental Africa for 1999, 2000, and 2001 were downloaded the from the African Data Dissemination Service (ADDS) site at [http://edcsnw4.cr.usgs.gov/adds/index.php], and were subset for southern Africa by the SAFARI 2000 data group. The RFE 1.0 algorithm, implemented from 1995 to 2000, uses an interpolation method to combine Meteosat and Global Telecommunication System (GTS) data, and warm cloud information for the 10-day estimations. The 30-minute geostationary Meteosat-7 satellite infrared data are used to estimate convective rainfall from areas where cloud top temperatures are less than 235K. The RFE 2.0 algorithm, implemented as of January 1, 2001, uses additional techniques to better estimate precipitation while continuing the use of cold cloud duration and station rainfall data. The 2.0 algorithm also incorporates two additional satellites, the Special Sensor Microwave/Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU) to further aid the estimation. Rain gauge data from around 1000 World Meteorological Organization (WMO) GTS stations that pass quality control procedures are weighted more heavily toward the final rainfall estimate as the distance to the station decreases. Thus, at a distance far from the rain gauge, satellite estimates data dominate the final output result.The RFE subsets are flat binary images, with no headers. The data are limited to the range 0-250 and the rainfall units are millimeters. The data are in an Albers projection, and the pixels are 8 km square. Each single-byte image is 928 samples by 711 lines. There are 3 images per month, thus 36 per year, for a total of 108 10-day rainfall images for the period 1999-2001.' description_attribution: http://daac.ornl.gov//S2K/guides/s2k_fews_rfe.html doi: 10.3334/ORNLDAAC/711 end_time: 2001-12-31T00:00:00 href: https://data.globalchange.gov/dataset/nasa-ornldaac-711.yaml identifier: nasa-ornldaac-711 lat_max: 10.0997 lat_min: -42.2825 lon_max: 50.5201 lon_min: 20.6417 name: 'SAFARI 2000 FEWS 10-day Rainfall Estimate, 8-Km, 1999-2001' native_id: s2k_fews processing_level: ~ publication_year: ~ release_dt: 2004-01-01T00:00:00 scale: ~ scope: ~ spatial_extent: ~ spatial_ref_sys: ~ spatial_res: ~ start_time: 1999-01-01T00:00:00 temporal_extent: ~ temporal_resolution: ~ type: ~ uri: /dataset/nasa-ornldaac-711 url: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=711 variables: ~ version: ~ vertical_extent: ~