Normalized Difference Vegetation Index (ENDVI10, LSA-453)


DOI for scientific and technical data:


Data Record available since 2008


The European Metop satellites were conceived by ESA/EUMETSAT to complement the geostationary METEOSATs. In this way they are analogue to the North-American NOAA-platforms which accompany the geostationary GOES-East and -West satellites. Since mid-2007, MetOp thus occupies the "morning orbit" while NOAA assumes the "noon orbit": the local solar time of the overpasses is around 9h30' for Metop and 14h for NOAA. Both platforms carry the same AVHRR instrument which scans the full earth surface at 1km resolution in five spectral bands: RED, NIR, SWIR, TIR1, TIR2. (During the night the SWIR-band is switched to MIR, but the ENDVI10 only deal with daytime registrations).
Compared to NOAA, the MetOp-AVHRR has been enhanced in three ways: the platform is perfectly stabilized which guarantees an optimal geo-correction of the imagery, all registered 1km data are stored on board and channelled via the antenna of Svalbard (Sweden) to the central processing centre of EUMETSAT (Germany), and the latter performs the most crucial enhancement steps (rectification, calibration, cloud/snow detection) and broadcasts the results in real-time via its EUMETCast system.
VITO ingests all the daytime registrations of MetOp-AVHRR and further processes them into global, 10-daily synthesis images, very comparable with the S10 of SPOT-VEGETATION.


Product Documentation

This released Data Record is documented in the following documents: 

Please see Product Peer-Review publications in References.

The use of LSA SAF products in publications is kindly requested to be duly acknowledged:
ENDVI was provided by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF; Trigo et al., 2011)

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 MetOp-AVHRR S10 or "ENDVI10" are near-global, 10-daily composite images which are synthesized from the "best available" observations registered in the course of every "dekad" by the orbiting earth observation system MetOp-AVHRR. The composites represent a Normalized Difference Vegetation Index and are distributed together with a set of ancillary dataset layers (surface reflectances, sun and view angles, quality indicators) as part of EUMETSAT LSA SAF program. The land surface temperature (LST) layer is added on courtesy of VITO, whereof EUMETSAT does not take any liability, responsibility and ownership. More details can be found at


Algorithm Description

The 1km resolution image data registered all over the globe by METOP-AVHRR are systematically captured by the antenna in Svalbard (Norway) and further channelled to EUMETSAT (Germany). EUMETSAT immediately applies some crucial pre-processing steps: the raw observations are calibrated and transformed into top-of-atmosphere radiances (TOA), accurate "Lon/Lat-planes" are added with the geographical position of each pixel in the raw segment, and also a mask is added indicating the status of each observation (clear, cloud, snow). The resulting data stream is cut into segments of 3 minutes (1080 scanlines) which are distributed in near-real time via the EUMETCast broadcasting system in the form of EPS-formatted Level1B-files.

The METOP EPS-files are ingested through the EUMETCast receiving antenna's (one base, one spare). However, for the production of the ENDVI10 only the daytime segments are used (hence band 3 is always SWIR). Each individual segment is then spatially corrected (remapping the segments to Geographical Lon/Lat system with the same framing/resolution (1°/112) as used for SPOT-VEGETATION, radiometric corrected (convert TOA-radiances to surface-level reflectances (Rs) by means of SMAC algorithm for atmospheric correction for shortwave bands and convert TOA-radiances into brightness temperatures through investion of Planck's law and combined in an advanced split-window approach for the longwave bands. An 8-bit status mask is added to indicate the quality of the observed pixel.

The last stage in the algorithm is to create the near-global 10-daily composites (ENDVI10). For a given dekad, all corrected segments are searched. Most often, different observations are available for each pixel, from different segments or registration dates. By means of a compositing rule the "best available" observation is selected, which is then written to the output image. But as each observation comprises different "layers" (reflectances, angles, NDVI etc.), also the composite will contain as many separate image layers. The compositing rule is a classical "Maximum NDVI" with constraints on the observation status and the registration geometry. More details can be found at


Data Characteristics

From a temporal aspect, every month is divided in three "dekads". The first two always comprise ten days (1-10, 11-20), the third one has variable length as it runs from day 21 until the end of the month. The distinction between "days" is based on UT/GMT criteria. And every "dekad" a new product is generated. Although MetOp-AVHRR became operational around mid-2007, the composite time series distributed by VITO only starts in January 2008. The objective is to deliver each new composite with a maximum delay of three days, i.e. at the latest on days 03/13/23.

From a spectral aspect, each composite comprises twelve separate image layers. The NDV layer represents the Normalized Difference Vegetation Index, while the other layers are considered as ancillary layers: synthesis reflectance values, viewing angles, status map.

The composites are transferred to the users in ZIP-form. Each ZIP thus comprises 26 files: twelve IMGs, twelve HDRs, one XML (INSPIRE compatible metadata) and one TIFF (a quicklook map of the NDVI layer).

More details can be found at


Product Uncertainties

Geometric Accuracy
EUMETSAT provides very accurate lat/lon-planes for the data from the METOP-AVHRR sensor. This accuracy is achievable because the attitude of the satellite is known. This is in contrast with the AVHRR data of the NOAA satellites whose attitude is unknown Clearly, this puts a limit on the attainable accuracy of the geometric correction. Location accuracy in the case of NOAA-AVHRR data depends strongly on the processing algorithms used as opposed to VGT. A locational accuracy of 1 km is achievable for NOAA-AVHRR image registration (Rosborough et al., 1994). For METOP-AVHRR, the accuracy is higher and probably of the same magnitude as for SPOT-VGT.

Calibration Accuracy
The radiometric performance of a sensor in the visible and near-infrared region usually degrades in orbit (e.g. Gutman, 1999). For AVHRR, the estimation of calibration coefficients is performed a posteriori. Sensor response is then vicariously determined with stable terrestrial targets whose radiances can be measured or inferred. For METOP-AVHRR, the same vicarious calibration method is used as for NOAA-AVHRR to guarantee similarity between data from the same sensor family.

Berthelot B, 2008, SMAC coefficients for METOP AVHRR/3, VEGA Technologies SAS, Toulouse, Internal report SMAC01-TN-AVHRR3-VEGA, 63 p. (SMAC coefficients for METOP-AVHRR:

Coll C and Caselles V,1997, A split-window algorithm for land surface temperature from Advanced Very High Resolution Radiometer data: validation and algorithm comparison, Journal of Geophysics Research, 102(D14), 16697-16713.

Cracknell A, 1997, The Advanced Very High Resolution Radiometer, Taylor & Francis, ISBN 0-7484-0209-8.

Eerens H, Baruth B, Bydekerke L, Deronde B, Dries J, Goor E, Heyns W, Jacobs T, Ooms B, Piccard I, Royer A, Swinnen E, Timmermans A, Van Roey T, Vereecken J & Verheijen Y, 2009, Ten-Daily Global Composites of Metop-AVHRR, Proc. of the 6th International Symposium on Digital Earth, Beijing, 9-12 September 2009.

Eerens H, Piccard I, Royer A and Orlandi S, 2004, Methodology of the MARS crop yield forecasting system. Vol. 3: Remote sensing information, data processing and analysis, Eds. Royer A and Genovese G, EUR 21291 EN/3, 76 p.



EUMETSAT, 2008, AVHRR Level1b Products Guide, EUMETSAT, Darmstadt, Germany, Document EUM/OPS-EPS/MAN/04/0029, 123 p. (AVHRR Level 1b Products Guide).

Gesell G, 1989, An algorithm for snow and ice detection using AVHRR data. An extension of the APOLLO software package, Int. J. Remote Sensing, 10(4-5): 897-905.

Hall D, Riggs G and Salomonson V, 1995, Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data, Remote Sensing of Environment, 54: 127-140.

Kidwell K (Editor), 1997, NOAA polar orbiter data user's guide, US Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data and Information Service, Washington, DC 20233, Revision January 1997. See:

Planet W (Editor), 1988, Data extraction and calibration of TIROS-N/NOAA radiometers, NOAA Technical Memorandum NESS 107, Revision 1, Oct. 1988, 130 p.

Rahman H and Dedieu G, 1994, SMAC: a Simplified Method for the Atmospheric Correction of Satellite Measurements in the Solar Spectrum, International Journal of Remote Sensing, 15(1): 123-143.

Rao C and Chen J, 1999, Revised post-launch calibration of the visible and near-infrared channels of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-14 spacecraft. Int. J. Remote Sensing, vol. 20, no. 18, p. 3485-3491.

Swinnen E, 2009, Factors affecting the difference between data from SPOT-VEGETATION and METOP-AVHRR internal report, VITO.

Swinnen E, Eerens H, Heyns W, Piccard I, Viaene P and Claes P, 2007, An integrated long time series of 1 km resolution NDVI for Europe from the NOAA-AVHRR and SPOT-VEGETATION sensors, In: Proceedings of MultiTemp 2007: 4th International workshop on the analysis of multi-temporal remote sensing images, 18-20 July, 2007, Leuven, Belgium. Eds. De Lannoy G et al., Leuven, Gent: Katholieke Universiteit Leuven, Universiteit Gent, 2007. ISBN 1-4244-0846-6.

Trishchenko, 2009, Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-18, 18 and METOP-A, Remote Sensing of Environment, vol. 113, No. 2, p. 335-341
van Diepen K, Boogaard H, Supit I, Lazar C, Orlandi S, Van der Goot E, Schapendonk A, Methodology of the MARS crop yield forecasting system. Vol. 2: Agrometeorological modelling, processing and analysis. Eds. Lazar C and Genovese G, EUR 21291 EN/2, 98 p.