This data set is only available for a restricted user group, please contact us if you want to access these data.
The Leaf Area Index (LAI) is defined as half the total area of photosynthetically active elements (e.g. leaves, needles) of the canopy per unit horizontal ground area. The LAI is a measure of the size of the interface for exchange of energy (including radiation) and mass between the canopy and the atmosphere. The LAI is strongly non-linearly related to reflectance. A satellite-derived LAI value corresponds to the total green LAI of all the canopy layers, including the understory which may represent a very significant contribution, particularly for forests. Practically, one can say that the LAI quantifies the thickness of the vegetation cover. For remote sensing of the LAI it is often assumed that the leaves are randomly distributed within the vegetation volume sensed and hence the retrieved LAI is an "effective" LAI being the best possible approximation of the actual LAI (see also the references).
Top of canopy input reflectance from SPOT-4 & -5 and - more recently - PROBA-V in red, near infrared and shortwave infrared bands, normalized over a period of 30 days are used in a neural network approach. Normalization over the 30-day period us done with a weighting function which favors the most recent daily observations of the compositing period. The neural network is trained with true (non-simulated) reflectance data and LAI estimates from fused existing products based on MODIS and CYCLOPES LAI products. Note that "clumping" or "clumpiness" (see Chen et al., 2005), i.e. the degree with which the leaves are not randomly distributed within the vegetation volume sensed, is not part of the data set offered here.
For more information we refer to the ATBDs mentioned in the references section.
This is version 2 of this data set provided by COPERNICUS Global Land Service.
Data were downloaded in netCDF file format with 1/112° grid cell resolution and global coverage. This data are available at ICDC on request. For the main data set offered here, we block-averaged the data onto a 0.5° x 0.5° plate carree grid. Data are available for the latitude range 70°S to 80°N. Note, however, that limited solar illumination reduces the maximum northern latitude to smaller values during winter.
Last update of the data set at ICDC: September 10, 2020.
|Leaf area index (LAI)||0 ... 7 (m²/m²)||resolution is 1/30|
|LAI uncertainty||0 ... 7 (m²/m²)||resolution is 1/30 (This is the RMSE between 10-day composit & daily values)|
|Number of land pixels||0 ... 3136||per 0.5° grid cell (see information in netCDF file)|
|Number of valid data pixels||0 ... 3136||per 0.5° grid cell (see information in netCDF file)|
|Mean number of usable satellite data||0 ... 120||per 0.5° grid cell|
|Cumulative number of usable satellite data||0 ... 188160||per 0.5° grid cell|
|Surface type flag||1, 10, 20, 40||Dominant (major) surface type|
|1: regular land, 10: evergreen broadleaf forest|
|20: bare soil / wetland / inland waters, 40: not defined|
|Retrieval flag||0 ... 24||Dominant (major) retrieval type|
|0: Invalid LAI value or no retrieval, 1: regular retrieval|
|2: As 1 but for high geographic latitude / solar zenith angle|
|11: As 1 but filled with climatology|
|12: As 11 but for high geographic latitude / solar zenith angle|
|14: Invalid LAI value, replaced by climatology|
|21: As 11 but filled with temporal interpolation, 22: respectively for 12, 24: respectively for 14|
Period and temporal resolution:
- 1998-11-10 to 2020-04-30
- 10-daily sampling, 60-day effective resolution
Spatial coverage and resolution:
- Spatial resolution: 0.5° x 0.5° (Plate Carree)
- Geographic latitude: 89.75°S to 89.75°N; data only between 70°S and 80°N
- Geographic longitude: 179.75°W to 179.75°E
- Dimension: 360 rows x 720 columns
- Altitude: follows topography
The original data set at 1/112° grid resolution contains a bit-wise to read quality flag. The content of the flag can be read under http://land.copernicus.eu/global/products/lai under "Technical".
The 0.5° x 0.5° data set offered here contains two basic separate flags. One flag refers to the two surface types where LAI retrieval is either impossible or prone to larger uncertainties. The other flag refers to the type of retrieval, i.e. whether data are interpolated or whether special corrections were applied to account for high latitude / high solar zenith angle illumination conditions. In addition the number of valid 1/112° x 1/112° grid points used to compute the average value of the LAI and its uncertainty is given. As can be seen in the parameters section we give number of land pixels, number of valid pixels, and number of usable satellite data to convey information about reliability. We recommend to read the text given in the netCDF files. Note that we excluded LAI data where the average (over the 0.5° x 0.5° grid cell) relative error is larger than 50%.
LAI products have been validated and inter-compared with independent observations - globally, regionally. for different biomes, and also with regard to the temporal development - and are reported to have a good quality, showing a spatial consistent global distribution of retrievals. Temporal profiles are very smooth and are highly consistent from year to year. More information about the validation efforts can be found in the validation reports listed under references.
We would like to mention that for continuing the SPOT-4 and -5 time series with PROBA-V some pre-processing needs to be done. Information about this given in this report.
Institut National de la Recherche Agronomique (INRA), France
email: frederic.baret (at) avignon.inra.fr
email: Bruno.Smets (at) vito.be
ICDC / CEN / University of Hamburg
email: stefan.kern (at) uni-hamburg.de
- Algorithm Theoretical Basis Document: ATBD_v2_issueI1.41
- Product User Guide for Version 2: PUM_v2_issue1.33
- Validationsreports for SPOT: QAR_v2_issueI2.01 and PROBA-V: QAR_v2_issueI1.40
- Scientific Quality Evaluation for version 1 & 2: SQE2018_v1v2_issueI1.00
- Scientific Quality Evaluation - Cross-cutting consistency: SQE2018_CCR_issueI1.00
- Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholczyk, P., and Smets, B. (2013), GEOV1: LAI, FAPAR Essential Climate Variables and FCover global times series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment, 137, 299–309.
- Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Weiss, M., Samain, O., Roujean, J. L. et al. (2007), LAI, fAPAR and fapar CYCLOPES global products derived from VEGETATION. Part 1: Principles of the algorithm. Remote Sensing of Environment, 110, 275-286.
- Camacho, F., Cernicharo, J., Lacaze, R., Baret, F., and Weiss, M. (2013), GEOV1: LAI, FAPAR Essential Climate Variables and FCover global time series capitalizing over existing products. Part 2: Validation and inter-comparison with reference products. Remote Sensing of Environment, 137, 310-329.
- Verger, A., Baret, F., and Weiss, M. (2014), Near real-time vegetation monitoring at global scale. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 3473-3481.
- Chen, J. M., Menges, C. H., and Leblanc, S. G. (2005), Global mapping of foliage clumping index using multi-angular satellite data. Remote Sensing of Environment, 97(4), 447-457.
- Tote, C., Swinnen, E., Sterckx, S., and 10 others (2018), Evaluation of PROBA-V Collection 1: Refined Radiometry, Geometry, and Cloud Screening. Remote Sensing, 10(9), 1375, http://doi.org/10.3390/rs10091375
- Munier, S., Carrer, D., Planque, C., Camacho, F., Albergel, C., and Calvet, J.-C. (2018), Satellite leaf area index: global scale analysis of the tendencies per vegetation type over the last 17 years. Remote Sensing, 10(3), 424, http://doi.org/10.3390/rs10030424
- 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.
Upon using this data please cite as follows:
Baret, F., et al. (2013). GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment, 137: 299-309
Camacho, F., et al. (2013). GEOV1: LAI, FAPAR Essential Climate Variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. Remote Sensing of Environment 137: 310-329
The product was generated by the land service of Copernicus, the Earth Observation programme of the European Commission. The research leading to the current version of the LAI product has received funding from various European Commission Research and Technical Development programmes. The product is based on SPOT/VGT 1km data ((c) CNES / PROBA-V 1km data ((c) ESA and distributed by VITO), last access date: August 26, 2020, provided on 0.5 degree x 0.5 degree plate carree grid in NetCDF file format by the Integrated Climate Data Center (ICDC, icdc.cen.uni-hamburg.de) University of Hamburg, Hamburg, Germany.