MSG 10-days Leaf Area Index (MTLAI, LSA-424)
NRT Product available since Jan 2013
Leaf Area Index (LAI) is a dimensionless variable [m2/m2], which defines an important structural property of a plant canopy. LAI is defined as one half the total leaf area per unit ground area (Chen and Black, 1992). It provides complementary information to the FVC, accounting for the surface of leaves contained in a vertical column normalized by its cross-sectional area. It defines thus the area of green vegetation that interacts with solar radiation determining the remote sensing signal, and represents the size of the interface between the vegetation canopy and the atmosphere for energy and mass exchanges. LAI is thus a necessary input for Numerical Weather Prediction (NWP), regional and global climate modelling, weather forecasting and global change monitoring. Besides, the LAI is relevant for Land Biosphere Applications such us agriculture and forestry, environmental management and land use, hydrology, natural hazards monitoring and management, vegetation-soil dynamics monitoring and drought conditions.
NEW: NRT Demonstration Product available for Indian Ocean region since Jun 2017
Product Documentation
This operational product is documented in the following documents:
- Product User Manual (PUM)
- Product Output Format (POF)
- Validation Report (VR)
- Algorithm Theoretical Basis Document (ATBD)
- Algorithm Changes Record
Please see Product Peer-Review publications in References.
Here you will find the daily Leaf Area Index averaged per month over the 2004-2019 period.
This dataset was derived by joining the LAI CDR (MDLAI-R) for 2004-2015 and the operational NRT product (MDLAI, LSA-423) for 2016-2019.
Example:
NETCDF4_LSASAF_CLIMA-12-2004-2019_MSG_LAI_MSG-Disk_200412010000.nc
This NetCDF file contains the average of daily LAI for all days of "December" over the 2004-2019 period. The datasets are available on a regular 0.05º grid and the files' format is fully CF-compliant.
The use of LSA SAF products in publications is kindly requested to be duly acknowledged:
LAI and MTLAI were provided by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF; Trigo et al., 2011)
http://lsa-saf.eumetsat.int
Trigo, I. F., C. C. DaCamara, P. Viterbo, J.-L. Roujean, F. Olesen, C. Barroso, F. Camacho-de Coca, D. Carrer, S. C. Freitas, J. García-Haro, B. Geiger, F. Gellens-Meulenberghs, N. Ghilain, J. Meliá, L. Pessanha, N. Siljamo, and A. Arboleda, 2011: The Satellite Application Facility on Land Surface Analysis. Int. J. Remote Sens., 32, 2725-2744, doi: 10.1080/01431161003743199
The LAI product is currently generated daily at the full spatial resolution of the MSG/SEVIRI instrument, and will be later provided on a 10-days and monthly basis. This product is based on the information provided by the three short-wave channels of SEVIRI (VIS 0.6 µm, NIR 0.8 µm, SWIR 1.6 µm), which are used as input for deriving the FVC product. The LAI product is thus obtained directly from the cloud-free FVC product, which has been corrected from view/sun angles and anisotropy effects. The LAI product ranges from 0 to 10. The LAI product includes routine quality check and error estimates. The product will be validated in order to define the product uncertainties over a range of global conditions.
Algorithm Description
The algorithm for retrieving LAI currently running in the LSA SAF system is the backup solution of a physical-model inversion method (García-Haro et al., 2005) to be implemented in the system in 2006. The backup algorithm employs a semi-empirical exponential relationship with the FVC product as in Roujean and Lacaze (2002). The algorithm assumes spherical orientation of the foliage and a coefficient which is function of the leaf albedo. Although this relationship is unique for all the biomes, the coefficients can be empirically fitted to different vegetation types. Here it is assumed a random distribution of the vegetation. This assumption leads to an effective LAI which under-estimates LAI in highly clumped vegetation such us boreal or tropical forest. However, the algorithms allows incorporating a clumping index to adjust clumping effects on LAI estimates. The overall LAI error is computed by propagation of the input error.
Data Characteristics
The LAI product is computed within the area covered by the MSG disk, over 4 specific geographical regions (Europe, Africa - N_Africa and S_Africa- and South America). For each day and geographical region, the LAI product, its error estimate and the processing flag are disseminated in HDF5 format. The relevant information concerning the data fields is included in the HDF5 attributes.
Product Uncertainties
Automatic Quality Control (QC) is performed on LAI product and the quality information is provided on a pixel basis. QC contains general information about input data quality, and specific information related with the limits of application. The error estimate defines the confidence level of the product on the basis of the semi-empirical approach considered and error propagation. The quality of LAI product depends thus basically on the FVC error, which depends on input quality (signal-to-noise ratio, accuracy of atmospheric corrections, number of cloud-free observations), accuracy of cloudy pixels identification, and spectral variation in reflectance of different land-surface elements. Due to its non-linear relationship with surface reflectance and the saturation effect found in canopy reflectance for LAI greater than about 3.0, the estimation of LAI given surface reflectance is an ill-posed problem, for which external prior information is very useful. Although LAI appears to be very sensitive to small variations in surface reflectance, the specification for the overall accuracy of the LAI product is 15%. Validation will include the assessment of the product in a systematic and statistically robust way representing global conditions. Results of detailed validation studies will be given in the Validation Report and later included in the Product User Manual.
García-Haro, F. J., Camacho, F., Martínez, B., Campos-Taberner, M., Fuster, B., Sánchez-Zapero, J., & Gilabert, M. A. (2019). Climate data records of vegetation variables from geostationary SEVIRI/MSG data: products, algorithms and applications. Remote Sensing, 11(18), 2103. DOI: 10.3390/rs11182103.
García-Haro, F.J., Camacho-de Coca, F., Meliá, J. (2006). DISMA A Directional Spectral Mixture Analysis method: Application to multi-angular airborne measurements. IEEE Transactions of Geoscience and Remote Sensing, 44(2), 365-377, DOI: 10.1109/TGRS.2005.861008.
Roujean, J.L. and R. Lacaze, (2002). Global mapping of vegetation parameters from POLDER multiangular measurements for studies of surface-atmosphere interactions: A pragmatic method and its validation. Journal of Geophysical Research, 107D, 10129-10145.
Chen, J. M. and T. A. Black, 1992. Defining leaf area index for non-flat leaves. Plant Cell Environment, 15: 421-429.