Abstract: MODIS Collection 6.1 yearly gap-filled Gross Primary Production (GPP) and Net Primary Production (NPP) data on the MODIS sinusoidal grid are taken from the netCDF files produced at ICDC, for which the bit-encoded quality information given in the HDF-files was already decoded, and re-gridded to build a global map of grid-cell mean GPP and NPP and their variances on a global equirectangular climate modeling grid (CMG). Only those GPP or NPP values are used where i) the cloud flag indicates either clear sky or assumed clear sky, where the MODLAND quality is good and where the confidence flag suggests best quality or good quality data. The confidence flag is provided as a grid-cell mean rounded value with fractions of the five original flags being provided for convenience. Cloud conditions are included in form of the primary cloud flag and the fraction this primary cloud flag occupies among the valid 500 m sinusoidal grid grid cells. Two separate layers of the number of valid grid cells of the 500 m sinusoidal grid are given, one is for the geophysical data and one is for the flags.

TableOfContents: grid cell mean Gross_Primary_Production (GPP); grid cell mean Net_Primary_Production (NPP); GPP standard deviation over grid cell; NPP standard deviation over grid cell; number of valid used GPP or NPP values per grid cell; number of valid used confidence and quality flag values per grid cell; grid cell mean confidence flag; fraction of confidence flag 0 in grid cell; fraction of confidence flag 1 in grid cell; fraction of confidence flag 2 in grid cell; fraction of confidence flag 3 in grid cell; fraction of confidence flag 4 in grid cell; primary cloud flag; primary cloud flag fraction

Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2001-01-01; temporalExtent_endDate: 2023-12-31; temporalResolution: Yearly; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA

Methods: [1] Running, S. W., and M. Zhao, Users Guide Daily GPP and Annual NPP (MOD17A2H/A3H) and Year-end Gap-Filled (MOD17A2HGF/A3HGF) Products NASA Earth Observing System MODIS Land Algorithm, (For Collection 6), Version 4.0, January 2, 2019; [2] Running, S. W., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto, A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 547-560, 2004; [3] Running, S. W., A measurable planetary boundary layer for the biosphere. Science, 337(6101), 1458-1459, 2012; [4] Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running, Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95(2), 164-176, 2005

Units: Units for all variables (see TableOfContents): kg C m-2; kg C m-2; kg C m-2; kg C m-2; 1; 1; 1; percent; percent; percent; percent; percent; 1; percent

geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land

Size: (files are packed into one zip-archive)

• 2001-2023: 1 file per year, each approximately 12975000 bytes

Format: netCDF

DataSources:

Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD17A3HGF.061 (last accessed: 2024-06-03), see also https://lpdaac.usgs.gov/products/mod17a3hgfv061/ (last accessed: 2024-06-03)

Data on sinusoidal grid tiles in netCDF format: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2024-07-09) or https://doi.org/10.25592/uhhfdm.14633 (last accessed: 2024-07-09).

Contact: stefan.kern (at) uni-hamburg.de

Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2024-07-09)

The MODIS/Terra LAI-FPAR Phenology annual L4 Global 1km SIN Grid product with short-name MOD15A2PHN, is estimated from MCD15A2 8-day products. The spatial resolution is 1-km. The MOD15PHN is stored in Hierarchical Data Format (HDF) in sinusodial projection, same as other standard MODIS land products. For the first 11 phenology parameters, only the first two seasons (marked as s1 and s2) are stored if there are more than one valid seasonal cycles detected. Valid seasonal cycles should begin within the year of interest and end before the end of the second year. There are 27 Science Data Sets (SDS) in the available phenology product.

The MODIS Leaf Area Index (LAI) Product is a global product produced every 8 days. The Leaf Area Index is estimated by a radiative transfer model assuming a given distribution of biome types within each pixel. The LAI Product was evaluated for the period of May 2002 to September 2002. The temporal pattern of spring greenup and fall senescence appeared reasonable across a large latitudinal transect from the Kenai Peninsula to the Arctic Coastal Plain. The temporal pattern also appeared reasonable across an elevational transect from Bonanza Creek Experimental Forest to Caribou Poker Creek Research Watershed to Eagle Summit. The positional accuracy and spatial pattern LAI was judged excellent by comparing the M2002 maximum LAI for the Survey Line Burn with a Landsat ETM+ image. However, there were two consistent problems with the LAI index at all spatial scales. First, a dip in maximum LAI during the green-up period most likely indicated cloud contamination of pixels. Second, the maximum 2002 LAI estimate was unrealistically high (>6.5) in many areas of Alaska. The accuracy of global estimates of leaf area and vegetation indices are suspect for high latitude areas due to several factors: 1) There is no tundra or taiga biome used in the leaf area index radiative transfer model. 2) Although a cloud-screen algorithm is applied on the front-end of processing, sub-pixel cloud contamination may occur over much of Alaska. 3) Subpixel broadleaf shrubs may lead to an overestimate of leaf area index and inflate vegetation indices.This dataset contains MOD15 leaf area index (LAI) and fraction of photosynthetically absorbed radiation (FPAR) for most of Alaska during the 2002 growing season. The data are in hdf format, with one file for each MODIS tile, for each 8-day composite period. The original quality control bits are included in each hdf file. The data are in the integerized sinusoidal projection with approzimately 1-km pixel size.

The files were submitted in winzip format. The naming convention is
productname.date.tile. For example: MOD15A2.A2002065.h10v02 is product MOD15A2, composite period starting at 2002065, for tile h10v02.

More metadata about each file is embedded in each hdf file and can be read using any hdf browser. This dataset contains MOD15 leaf area index (LAI) and fraction of photosynthetically absorbed radiation (FPAR) for most of Alaska during the 2002 growing season. The data are in hdf format, with one file for each MODIS tile, for each 8-day composite period. The original quality control bits are included in each hdf file. The data are in the integerized sinusoidal projection with approzimately 1-km pixel size.

The files were submitted in winzip format. The naming convention is
productname.date.tile. For example: MOD15A2.A2002065.h10v02 is product MOD15A2, composite period starting at 2002065, for tile h10v02.

More metadata about each file is embedded in each hdf file and can be read using any hdf browser.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Vegetation Index and Phenology (VIP) global datasets were created using surface reflectance data from the Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 – 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIPPHEN data product is provided globally at 0.05 degree (5600 meter) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIPPHEN phenology product contains 26 Science Datasets (SDS) which include phenological metrics such as the start, peak, and end of season as well as the rate of greening and senescence. The product also provides the maximum, average, and background calculated VIs. The VIPPHEN SDS are based on the daily VIP product series and are calculated using a 3-year moving window average to smooth out noise in the data. A reliability SDS is included to provide context on the quality of the input data.

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Reflectance VNIR and SWIR (AST_07) data product contains measures of the fraction of incoming solar radiation reflected from the Earth's surface to the ASTER instrument corrected for atmospheric effects and viewing geometry for both the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) sensors. The AST_07 product has a spatial resolution of 15 meters (m) for the VNIR bands and 30 m for the SWIR bands. Each product delivery includes two Hierarchical Data Format - Earth Observing System (HDF-EOS) files: one for the VNIR, and the other for the SWIR. They are distinguished from one another by a one-second difference in the production time that appears as part of the file name. The ASTER L2 Surface Reflectance VNIR and SWIR data product is only available through NASA's Earthdata Search. The ASTER Order Instructions provide step-by-step directions for ordering this product.Known Issues Data Anomalies: Users are advised that ASTER SWIR data acquired from April 2008 to the present exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details. Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions.Improvements/Changes from Previous Version The Science Scalable Scripts-based Science Processor for Missions (S4PM) Version 3.4 algorithm, which is used to generate L2 Product Generation Executables (PGEs), is relying on a new ancillary input for atmospheric parameters. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is global atmospheric reanalysis that combines remote sensing observations and interactions with the climate system. It will be one of the primary ozone and water vapor, pressure, and temperature inputs for L2 PGEs. MERRA-2 will provide a finer geographic resolution grid since it is a 3-dimensional, 3-hourly data collection with 50-km (latitudinal direction) spatial resolution. The fallback options for L2 PGEs are as follows: * Ozone: [TOVS Ozone (OZ_DLY ) > AURA Ozone Monitoring Instrument (AURAOMI) > Total Ozone Analysis from Stratospheric and Tropospheric (TOAST) > Earth Probe-Total Ozone Mapping Spectrometer (EPTOMS)] or [MERRA-2] > National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS) > Climatology * Water Vapor, Pressure, and Temperature: [MOD07_L2] or [MERRA-2] > NCEP/GDAS > Climatology Caveat: The temporal range for MERRA-2 covers 1980 to present; however, there is latency of ~3 weeks after the end of a month. Hence, NCEP/GDAS > Climatology fallback sequence will be applied for on-demand requests that fall outside of MERRA-2's temporal range or if the data is not science grade. Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427. As of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include: * Aura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same. * Toolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data.Additionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be removed from the Earthdata Order Form. It has been observed that PGEs generated with Climatology as an input yield noticeable differences statistically during image and spectral analysis. Climatology will continue to be used as the final default if neither of the first two selectable options are available for Ozone and MTP. Users can check the OPERATIONALQUALITYFLAGEXPLANATION field in the metadata or the output file for atmospheric parameters that were applied.

The data is comprised of 20 .hdf files of the X-ray projections recorded during isothermal annealing of Zn-Mg samples, at discrete time-steps shown below for files names ending in ‘...30141’ to ‘…30161’:

30141: prior to annealing; 30142: 1 min annealing; 30143: 3 min; 30144: 5 min; 30145: 7 min; 30146: 10 min; 30147: 15 min; 30148: 20 min; 30150: 31 min; 30151: 1 hr; 30152: 2 hr; 30153: 3 hr; 30154: 4 hr; 30155: 5 hr; 30156: 6 hr; 30157:7 hr; 30158: 8 hr; 30159:9 hr; 30160: 9 hr, 10 min; 30161: 10 hr

The raw data file is in .hdf format and can be reconstructed into .tiff, e.g., by using the TomoPy toolbox in Python.