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Data access via file system /data/icdc/land/modis_lai_fpar
Atmospheric influence corrected surface reflectances of up to 7 spectral bands of reflectance measurements of the MODIS sensors aboard both EOS platforms: TERRA and AQUA, are used to derive the Leaf Area Index (LAI) and the fraction of the absorbed (by green vegetation) photosynthetically active, i.e. 400-700 nm, radiation (FAPAR). For high enough solar elevation angels a radiative transfer modeling (RTM) approach is used. For small solar elevation angles and/or if the main algorithm based on the RTM fails due to other reasons an empirical back-up algorithm is used which - like the RTM based one - includes MODIS and other land cover products and the respective distribution of vegetation types (biomes) but which is less physical in the setup.
The key features of the RTM based approach as well as of the empirical algorithm are described in the Algorithm Theoretical Basis Document (ATBD). This approach has been developed further since the first MODIS LAI data set release, which was collection 3); the current version belongs to the Collection 5 suite of MODIS data products. An extensive literature list about the different retrieval stages can be found here: http://cliveg.bu.edu/modismisr/atbds/modisdocs.html .
One of the key improvements towards collections 5 and 6 (see change report) was the inclusion of more biomes; different vegetation causes different canopy reflectance and a more detailed and more reliable classification into different biomes was found to increase success for LAI and FPAR retrieval.
We have the entire data set MOD15A2 available - organized into tiles and also gridded onto a global 0.5 degree x 0.5 degree resolution climate modeling grid (CMG). Currently only the data of one tile: h18v03, see http://icdc.cen.uni-hamburg.de/daten/land/modis-srtm-landwaterdistribution.html is online and visualized. Data of all other tiles are also available in netCDF file format, e.g. via mistralpp.dkrz.de or upon request.
Last update of this data set at ICDC: February 20, 2020.
|Name||Unit||Comments & valid values|
|Fraction of photosynthetically active (400-700nm) i.e. absorbed by green vegetation radiation (FAPAR)||%||0 ... 1|
|Leaf area index = Lead area per unit ground area (LAI)||m² / m²||0 ... 10|
|FAPAR retrieval standard deviation||--|
|LAI retrieval standard deviation||m² / m²|
|Detailed quality flag||--||0,1,5,6,9; added to 0, 10, 20, 50, 100, 110, 120, and 150, respectively|
|Quality flag for land||--||0, 10, 20, 40, 60, 100, 120, 140, 200, 220, 240|
|Quality flag for cloud & aerosol||--||0,1,10,11,20,21,30,31,100,101,110,111,120,121,200,201|
|Quality flag method||--||0, 1, 2 (0: no retrieval; 1: very good, main method; 2: good, empirical method)|
|Climate modeling grid:|
|Mean fraction of photosynthetically active (400-700nm) i.e. absorbed by green vegetation radiation (FAPAR)||%||0 ... 1|
|Mean leaf area index = Leaf area per unit ground area (LAI)||m² / m²||0 ... 10|
|Variance of mean FAPAR||%||0 ... 1|
|Variance of mean LAI||m^4 / m^4||0 ... 10|
|Mean FAPAR retrieval standard deviation||--|
|Mean LAI retrieval standard deviation||m² / m²|
|Number of valid FAPAR or LAI values per grid cell||--||0 ... 14196|
|Number of valid retrieval standard deviation values per grid cell||--||0 ... 14196|
|Primary (most common) quality flag in grid cell||--||0 ... 700|
|Secondary (2nd most common) quality flag in grid cell||--||0 ... 700|
|Primary (most common) land surface type in grid cell||--||0 ... 900|
|Secondary (2nd most common) land surface type in grid cell||--||0 ... 900|
|Fraction of pixels with cloud cover||%||0 ... 100|
|Fraction of pixels with considerable aerosol load||%||0 ... 100|
Time period and temporal resolution:
- 2000/02 - 2019/12
Spatial coverage and resolution:
- Global or split into tiles of ~10° latitude and 10+° longitude (see image)
- Spatial resolution: Climate Modeling Grid (CMG): 0.5° x 0.5°; tiles: 1km x 1km, MODIS sinusoidal grid
- Geographic Latitude: CMG: -89.75 to 89.75; tiles: depends on tile
- Geographic Longitude: CMG: -179.75 to 179.75; tiles: depends on tile
- Dimension: CMG: 360 rows x 720 columns; tiles: 1200 rows x 1200 columns per tile
- Altitude: following terrain
The data set contains retrieval uncertainties for both parameters LAI and FAPAR.
The data set contains in addition a number of quality flags.
The most simple one distinguishes between the methods used and allows us to discriminate between areas with very good retrievals, where the main method based on radiative transfer modeling was used and areas with "just" good retrievals, where the main method needed to be replaced by an empirical method because, e.g., a too low solar elevation. This latter issue is the reason why in some maps - depending on geographic latitude and season - LAI and FAPAR retrieval standard deviations are missing.
The other quality flags (3 layers) give information about the land cover other than the biomes used, e.g. whether snow has been detected or whether the area is mainly a built-up one, about the influence of clouds and aerosol (with or without aerosol or clouds, cirrus or no cirrus, cloud shadows, etc.) and for those who prefer really detailed information there is a detailed quality flag - where one can find for instance where a pixel was just assumed to be cloud free.
The references list selected literature dealing with MODIS LAI and FAPAR evaluation - mainly for the collection 5 MODIS LAI & FAPAR data. The paper by Disney et al. (2016) contains a good overview about differences in methodology between different approaches and points to the potential limitations of this data set.
We recommend to also take a look at the comments about data quality for the combined AVHRR-LAI data set.
Climate and Vegetation Research Group
Department of Earth and Environment, Boston University, MA, U.S.A.
email: myneni (at) bu.edu
ICDC / CEN / University of Hamburg
email: stefan.kern (at) uni-hamburg.de
- MODIS LAI/FPAR Product Users's Guide
- MODIS LAI/FPAR ATBD v4.0
- MODIS LAI/FPAR C005 Changes
- Disney, M., et al., 2016, A new global fAPAR and LAI data set derived from optimal albedo estimates: Comparison with MODIS products, Rem. Sens., 8, 275, doi:10.3390/rs8040275
- Fang, H., et al., 2013, Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: Analysis of climatologies and theoretical uncertainties, J. Geophys. Res. - Biogeosciences, 118, 529-548, doi:10.1002/jgrg.20051.
- Fang, H., et al., 2013, The impact of potential land cover misclassification on MODIS Leaf Area Index (LAI) estimation: A statistical perspective, Remote Sens., 5, 830-844, doi:10.3390/rs5020830.
- Fang, H., et al., 2012, Theoretical uncertainty analysis of global MODIS, CYCLOPES and GLOBCARBON LAI products using a triple collocation method, Remote Sens. Environ., 124, 610–621.
- Liu, R., 2017, Compositing the minimum NDVI for MODIS data. Trans. Geosci. Rem. Sens., 55(3), 1396-1406.
- Yang, W., et al., 2006, Analysis of leaf area index products from combination of MODIS Terra and Aqua data, Remote Sens. Environ., 104, 297-312.
- Zhang, Y., et al., 2017, Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening? Rem. Sens. Environ., 191, 145-155.
- Jiang, C., et al., 2017, Inconsistencies of interannual variability and trends om long-term satellite leaf-area index products. Global Change Biology, 23, 4133-4146, doi: 10.1111/gcb.13787.
Please cite the data as follows (please choose correction option from ):
MODIS LAI and FPAR data from Myneni, R., Knyazikhin, Y., Park, T. (2015). MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. http://doi.org/10.5067/MODIS/MOD15A2H.006 [last access date: January 28, 2020], distributed by the Land Processes Distributed Active Archive Center (LP DAAC), located at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center (lpdaac.usgs.gov), are provided in netCDF format [on tiles][on 0.5 degree global climate modeling grid] by the Integrated Climate Data Center (ICDC, icdc.cen.uni-hamburg.de) University of Hamburg, Hamburg, Germany.