OPeNDAP Dataset Query Form

OPeNDAP Dataset Access Form

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Data URL:

Global Attributes:	Conventions: CF-1.6 title: MODIS 8-day Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) from LPDAAC: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod15a2 gridded from sinusoidal grid into an equi-rectangular (Climate Modeling Grid) grid summary: Original FPAR and LAI data are read together with their bit-encoded quality information; this information is decoded and provided in form of separate flag layers to the user of the tile netCDF files produced at ICDC; These are then read and re-gridded to build a global map of LAI, FPAR and other parameters; only those LAI of FPAR values are used where i) the cloud flag indicates a maximum of two cloud influence, and where ii) cloud cover is clearly defined, i.e. assumed clear sky is not used; flag layers are summarized such that there is gridded information about 1) cloud fraction, 2) fraction of average and high aerosol load, 3) primary and secondary landsurface type, 4) primary and secondary quality flag; primary and secondary refers to the highest and second highest pixel count of the respective type or flag within the grid cell. Note that the numbers of valid values differ for the grid cell mean LAI and FPAR and their variance, and for the grid cell mean retrieval standard deviation. institution: Center for Earth System Research and Sustainability (CEN), Integrated Climate Data Center, University of Hamburg creator_url: https://www.cen.uni-hamburg.de/icdc creator_name: Integrated Climate Data Center (ICDC) creator_email: stefan.kern (at) uni-hamburg.de date_created: Wed Mar 20 08:12:09 2024 StartYear: 2000 EndYear: 2023 StartDate: 2000-02-18 EndDate: 2023-12-31 Period: 2000-2023 cdm_data_type: Equirectangular, Climate Modeling Grid (CMG) geographic_region: global geospatial_lat_min: -89.7500 geospatial_lat_max: 89.7500 geospatial_lon_min: -179.750 geospatial_lon_max: 179.750 time_coverage_start: 20231219 time_coverage_end: 20231219+7 days time_coverage_duration: P24Y time_coverage_resolution: P8D standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata Convention geospatial_lon_resolution: 0.5 geospatial_lat_resolution: 0.5 platform: EOS-TERRA sensor: MODIS source: https://modis-land.gsfc.nasa.gov/lai.html and https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html references: [1] MODIS Collection 6 (C6) LAI/FPAR Product Users Guide, 24-February-2015, https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/mod15_user_guide.pdf; [2] Myneni, R. B., et al., Algorithm Theoretical Basis Document, v4.0, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf [3] Yang et al., MODIS Leaf Area Index Products: From Validation to Algorithm Improvement. IEEE Trans. Geosci. Remote Sens., 44: 1885-1898, 2006; [4] Morisette et al., Validation of global moderate resolution LAI products: A framework proposed within the CEOS Land Product Validation subgroup, IEEE Trans. Geosci. Rem. Sens., 44: 1804-1817, 2006; [5] Garrigues et al., Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, doi:10.1029/2007JG000635, 2008 citation: Globally gridded MODIS LAI and FPAR data at 0.5 resolution derived at ICDC (https://www.cen.uni-hamburg.de/icdc) from Myneni, R., Knyazikhin, Y., Park, T. (2021). MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V061. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD15A2H.061 [last accessed March 14, 2024] comment: Latitude and longitude coordinates per grid cell of the input tiles were computed at UHH based on the work of Benjamin Bechtel, benjamin.bechtel (at) uni-hamburg.de

Variables:	lat: Array of 32 bit Reals [lat = 0..359] lat: standard_name: latitude long_name: latitude coordinate units: degrees_north
	lon: Array of 32 bit Reals [lon = 0..719] lon: standard_name: longitude long_name: longitude coordinate units: degrees_east
	time: Array of 64 bit Reals [time = 0..1080] time: standard_name: time units: days since 2000-01-01
	fpar: Grid time: lat: lon: long_name: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) units: percent missing_value: 255.0 _FillValue: 255.0 coordinates: lat lon time
	lai: Grid time: lat: lon: long_name: Leaf Area Index (LAI) units: m^2 / m^2 missing_value: 255.0 _FillValue: 255.0 coordinates: lat lon time
	fpar_var: Grid time: lat: lon: long_name: FAPAR variance over grid cell units: percent missing_value: 255.0 _FillValue: 255.0 coordinates: lat lon time
	lai_var: Grid time: lat: lon: long_name: LAI variance over grid cell units: m^4 / m^4 missing_value: 255.0 _FillValue: 255.0 coordinates: lat lon time
	fpar_sdev: Grid time: lat: lon: long_name: FAPAR mean grid cell retrieval standard deviation units: percent missing_value: 255.0 _FillValue: 255.0 coordinates: lat lon time
	lai_sdev: Grid time: lat: lon: long_name: LAI mean grid cell retrieval standard deviation units: m^2 / m^2 missing_value: 255.0 _FillValue: 255.0 coordinates: lat lon time
	numberofvalidvalues: Grid time: lat: lon: long_name: number of useful LAI or FAPAR values per grid cell units: 1 missing_value: -32767 _FillValue: -32767 coordinates: lat lon time
	numberofvalidsdevvalues: Grid time: lat: lon: long_name: number of useful LAI or FAPAR retrieval standard deviation values per grid cell units: 1 missing_value: -32767 _FillValue: -32767 coordinates: lat lon time
	primary_qualityflag: Grid time: lat: lon: long_name: primary quality flag units: 1 missing_value: 32767 _FillValue: 32767 flag_values: [0 ... 100, 200 ... 300, 400 ... 500, 600 ... 700] flag_descriptions: 0 ... 100: --> 0 to 100% fraction of best quality: main (RT) method used, no saturation, mostly clear sky, if at all clouds WERE present in pixel; 200 ... 300: --> 0 to 100% fraction of good quality: main (RT) method used, but saturation possible, clouds were or are present or undefined cloud state / clear sky was assumed; 400 ... 500: --> fraction of useful quality: main (RT) method failed, empirical algorithm used, all sky conditions; 600 ... 700: --> 0 to 100% of not useful quality: pixel not produced comment: This the percent fraction of the type of the primary per pixel retrieval quality coordinates: lat lon time
	secondary_qualityflag: Grid time: lat: lon: long_name: secondary quality flag units: 1 missing_value: 32767 _FillValue: 32767 flag_values: [0 ... 100, 200 ... 300, 400 ... 500, 600 ... 700] flag_descriptions: 0 ... 100: --> 0 to 100% fraction of best quality: main (RT) method used, no saturation, mostly clear sky, if at all clouds WERE present in pixel; 200 ... 300: --> 0 to 100% fraction of good quality: main (RT) method, but saturation possible, clouds were or are present or undefined cloud state / clear sky was assumed; 400 ... 500: --> fraction of useful quality: main (RT) method failed, empirical algorithm used, all sky conditions; 600 ... 700: --> 0 to 100% of not useful quality: pixel not produced comment: This the percent fraction of the type of the secondary per pixel retrieval quality coordinates: lat lon time
	primary_surfacetype: Grid time: lat: lon: long_name: primary land cover units: 1 missing_value: 32767 _FillValue: 32767 flag_values: [0 ... 100, 200 ... 300, 400 ... 500, 600 ... 700, 800 ... 900] flag_descriptions: 0 ... 100: --> 0 to 100% fraction of previously non-flagged salt or inland freswater, of wetlands and marshlands, and of coastal areas; 200 ... 300: 0 to 100% fraction of regular landcover (note that barren and urban are separated already in the native product); 400 ... 500: 0 to 100% fraction of snow or ice on the ground; 600 ... 700: 0 to 100% fraction of urban areas; 800 ... 900: 0 to 100% fraction of barren, barely vegetated areas comment: This is the percent fraction of the primary land cover type within the grid cell (highest pixel count) coordinates: lat lon time
	secondary_surfacetype: Grid time: lat: lon: long_name: secondary land cover units: 1 missing_value: 32767 _FillValue: 32767 flag_values: [0 ... 100, 200 ... 300, 400 ... 500, 600 ... 700, 800 ... 900] flag_descriptions: 0 ... 100: --> 0 to 100% fraction of previously non-flagged salt or inland freswater, of wetlands and marshlands, and of coastal areas; 200 ... 300: 0 to 100% fraction of regular landcover (note that barren and urban are separated already in the native product); 400 ... 500: 0 to 100% fraction of snow or ice on the ground; 600 ... 700: 0 to 100% fraction of urban areas; 800 ... 900: 0 to 100% fraction of barren, barely vegetated areas comment: This is the percent fraction of the secondary land cover type within the grid cell (second highest pixel count) coordinates: lat lon time
	cloudfraction: Grid time: lat: lon: _Unsigned: false long_name: cloud fraction units: percent missing_value: 127 _FillValue: 127 comment: This is the percent fraction of native pixels with clouds or cloud shadows present coordinates: lat lon time
	aerosolfraction: Grid time: lat: lon: _Unsigned: false long_name: aerosol fraction units: percent missing_value: 127 _FillValue: 127 comment: This is the percent fraction of native pixels with average or high aerosol levels coordinates: lat lon time

For questions or comments about this dataset, contact the administrator of this server [Support] at: icdc.cen@lists.uni-hamburg.de

For questions or comments about the OPeNDAP service bundled with the TDS, email THREDDS support at: support-thredds@unidata.ucar.edu

DDS:

Dataset {
    Float32 lat[lat = 360];
    Float32 lon[lon = 720];
    Float64 time[time = 1081];
    Grid {
     ARRAY:
        Float32 fpar[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } fpar;
    Grid {
     ARRAY:
        Float32 lai[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } lai;
    Grid {
     ARRAY:
        Float32 fpar_var[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } fpar_var;
    Grid {
     ARRAY:
        Float32 lai_var[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } lai_var;
    Grid {
     ARRAY:
        Float32 fpar_sdev[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } fpar_sdev;
    Grid {
     ARRAY:
        Float32 lai_sdev[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } lai_sdev;
    Grid {
     ARRAY:
        Int16 numberofvalidvalues[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } numberofvalidvalues;
    Grid {
     ARRAY:
        Int16 numberofvalidsdevvalues[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } numberofvalidsdevvalues;
    Grid {
     ARRAY:
        Int16 primary_qualityflag[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } primary_qualityflag;
    Grid {
     ARRAY:
        Int16 secondary_qualityflag[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } secondary_qualityflag;
    Grid {
     ARRAY:
        Int16 primary_surfacetype[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } primary_surfacetype;
    Grid {
     ARRAY:
        Int16 secondary_surfacetype[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } secondary_surfacetype;
    Grid {
     ARRAY:
        Byte cloudfraction[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } cloudfraction;
    Grid {
     ARRAY:
        Byte aerosolfraction[time = 1081][lat = 360][lon = 720];
     MAPS:
        Float64 time[time = 1081];
        Float32 lat[lat = 360];
        Float32 lon[lon = 720];
    } aerosolfraction;
} modis_lai;

OPeNDAP Dataset Access Form

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