OLIVE available products for inter comparison

 

Product Name  OLIVE ID Variable Period Temporal Resolution Spatial Resolution Data Originator
GEOLAND, Version1 GEO_V01_1998_2012

LAI,FAPAR,FCOVER

1998-2012
10 days 1/112° EMMAH, INRA
CYCLOPES, V3.1  CYC_V31_1999_2008 LAI,FAPAR,FCOVER 1999-2008 10 days 1/112° EMMAH, INRA
GLOBCARBON  GLO_V01_1998_2004 LAI 1998-2004 1 month 1/112° EMMAH, INRA
Terra MODIS, Collection 5 MOD_C05_2000_2011 LAI,FAPAR 2000-2010 8 days 1km EMMAH, INRA
MGVI, Version 1
MGV_V01_2002_2012
FAPAR 2002-2012 10 days 1km EMMAH, INRA

 

GEOLAND, Version 1

GEOLAND Version1 (GEOV01), distributed by http://www.geoland2.eu/portal/, is derived from SPOT/VEGETATION sensors with a 10-day temporal sampling and 1/112° (about 1km at equator) ground sampling distance, in a Plate Carrée projection for the period 1998 (December) up to now (Baret et al. 2010; Baret et al. 2012).
Algorithm inputs are the same as for CYCLOPES, i.e, atmospherically corrected red, near-infrared (NIR) and shortwave-infrared (SWIR) reflectances; normalized to a standard view-illumination geometry and with automatic outlier rejection (Hagolle et al., 2005). The directional normalization is achieved by inversion of a reflectance model (Roujean et al., 1992) over data acquired during a 30 days compositing window period shifted every 10 days. GEOLAND version1 products are estimated using a neural network trained on values issued from the fusion of CYCLOPES and MODIS Collection 5 products to take advantage of their specific performances while limiting the situations where they show deficiencies. The learning data base was thus composed of the BELMANIP2 extracts of CYCLOPES L3A products and the weighted sum of corresponding CYCLOPES and MODIS LAI and fAPAR products.


For LAI, clumping at the canopy scale is intrinsically represented in the GEOLAND algorithm, since the neural networks are learned on MODIS product.


FAPAR products correspond to the black sky values at 10:00 solar time.


For FCOVER, since only the CYCLOPES products were available globally, no fusion with other products was possible. However, several evaluations have shown that CYCLOPES FCOVER products were suffering from a significant systematic underestimation (Weiss et al, 2007). This was corrected by applying a scaling factor to the CYCLOPES V3.1 in the learning data base.


A quality assessment flag (QA) value indicates when the retrieval algorithm fails, i.e. when there are less than two cloud- and snow-free observations in the compositing period (30 days) or when the retrieval is out of valid range. Therefore, for GEOLAND, QA=0 indicates that the data is valid and QA=1 indicates that the data was not produced.


GEOLAND Version 1 BELMANIP2 and DIRECT site extraction was performed by EMMAH, INRA.

  MGVI (Version 1)

MGVI (Meris Global Vegetation Index) is a fAPAR product distributed by ESA, derived from MERIS sensor with a 10-day temporal sampling for the period 2002 (June) up to 2012 (April). (Gobron et al, 1999, Gobron 2011). Resolution, projection?
Algorithm inputs are the daily MERIS top of atmosphere reflectances in the blue (band2), red (band 8), near-infrared (band 14). The MGVI Version 1 is then computed from the formulae provided in Gobron et al, 1999. The 10 day synthesis is performed by following Pinty et al (2002) that consists in selecting the MGVI value that is the closest from the average valid data over the 10 day synthesis. Valid data are identified using the MERIS L1b flag. Note that this algorithm was updated in 2004 and 2011, and MGVI version 2 will be further available in OLIVE.


FAPAR products correspond to the instantaneous black sky value at the time of satellite observations (about 10:30 solar time).

A quality assessment flag (QA) value indicates when the retrieval algorithm fails, i.e. when no valid data is available within the 10 day syntheis. Therefore, for MGVI, QA=0 indicates that the data is valid and QA=1 indicates that the data was not produced.


MERIS TOA reflectances for BELMANIP2 and DIRECT site were extracted by Brockmann Consult and projected in the UTM (WGS84) at 1km resolution, MGVI computation and synthesis was performed by EMMAH, INRA.

 

  CYLOPES, V3.1

CYCLOPES Version 3.1, distributed by http://postel.mediasfrance.org, is derived from SPOT/VEGETATION sensors with a 10-day temporal sampling and 1/112° (about 1km at equator) ground sampling distance, in a Plate Carrée projection for the period 1999 until 2007 (Baret et al., 2007).

Algorithm inputs are atmospherically corrected red, near-infrared (NIR) and shortwave-infrared (SWIR) reflectances; normalized to a standard view-illumination geometry and with cloud or snow cover observations removed (Hagolle et al., 2005). The directional normalization is achieved by inversion of a reflectance model (Roujean et al., 1992) over data acquired during a moving window compositing period of 30 days displaced by 10 days steps. CYCLOPES products are estimated using a neural network trained from one-dimensional radiative transfer model (SAIL Verhoef, 1984 and 1985) simulations. Estimations account for an overall uncertainty of 0.04 on reflectances including both model and measurement sources.

For LAI, clumping at the plant and canopy scale is not represented in the CYCLOPES algorithm. Landscape clumping is partly taken into account by considering mixed pixel as a fraction of pure vegetation and pure bare soil when simulating the VEGETATION surface reflectance at the pixel level with the SAIL and PROSPECT radiative transfer models (Jacquemoud et al., 2009).

FAPAR products correspond to the values for 10:00 solar time.

FCover product is also generated in the same way.

A quality assessment flag (QA) value indicates when the retrieval algorithm fails, i.e. when there are less than two cloud- and snow-free observations in the compositing period (30 days) or when the retrieval is out of valid range ([0 8]). Therefore, for CYCLOPES, QA=0 indicates that the data is valid and QA=1 indicate sthat the data was not produced.

CYCLOPES BELMANIP2 and DIRECT site extraction was performed by EMMAH, INRA.

 

  GLOBCARBON

GLOBCARBON Version 1 products distributed by http://geofront.vgt.vito.be/geosuccess/ is estimated for the period 1998-2004 from the combination of VEGETATION and AATSR (or the equivalent ATSR-2 for the 1998-2002 period for which AATSR was not available) observations.

Individual estimates of LAI and FAPAR are produced for all valid pixels from each sensor (atmospherically corrected, free from cloud, cloud shadow and snow) and then the median is computed at a 10-day time step from all available values from all sensors. The 10-day values are then subjected to a smoothing and interpolation procedure (Chen et al., 2006): The products are smoothed by averaging over a 1 month period and aggregated to 1/11.2° GSD (about 10km at equator) in a Plate Carrée projection.

The GLOBCARBON algorithm (Deng et al., 2006) relies on landcover specific relationships between effective LAI and combinations of red, NIR and SWIR spectral bands, derived using the Four Scale canopy reflectance model (Chen and Leblanc, 1997). Actual LAI is then derived, accounting for the clumping at plant and canopy scales by application of a cover-type dependent clumping index defined by Chen et al., 2005. The global application of the algorithm is achieved using the GLC2000 global land cover map (Bartholome and Belward, 2005).

FAPAR is derived from the effective LAI values, accounting for soil and leaf optical properties as well as some average extinction coefficient. It corresponds to the black sky instantaneous value at the time of the satellite overpass. A flag (QA) value indicates when no product estimate is computed, i.e. when there are no clear observations and for particular GLC2000 classes (burnt and bare area, snow and ice area, artificial surfaces). Therefore, for GLOBCARBON, QA=0 indicates that the data is valid and QA=1 indicates that the data was not produced.

GLOBCARBON BELMANIP2 and DIRECT site extraction was performed at EMMAH, INRA.

 

  TERRA MODIS COLLECTION 5

Terra MODIS LAI and FAPAR Collection 5 products (Myneni et al, 2002) are available from https://wist.echo.nasa.gov/api/. Collection 5 was produced between February 2000 through present at a 8-day time step and 1km GSD over an integerized sinusoidal grid.

MODIS main algorithm is based on Look Up Tables (LUT) simulated from a three-dimensional radiative transfer model (Knyazikhin et al., 1998). Red and NIR atmospherically corrected MODIS reflectance (Vermote et al., 1997) and the corresponding illumination-view geometry are used as inputs of the LUT. The output is the mean LAI and FAPAR values computed over the set of acceptable LUT elements for which simulated and MODIS surface reflectances agree within specified level of (model and measurement) uncertainties. When the main algorithm fails, a backup algorithm based on NDVI (Normalized Difference Vegetation Index) relationships, calibrated over the same radiative transfer model simulations is used (Yang et al., 2006). MODIS LAI retrieval is executed irrespective of cloud and snow state but the majority of retrievals under these conditions or with residual atmospheric contamination are performed with the back-up algorithm (Yang et al., 2006.

The main and backup algorithms are defined for 6 vegetation types as considered by the MODIS land cover map product (Friedl et al., 2002). MODIS algorithm accounts for vegetation clumping at the canopy and leaf (shoot) scales through 3D radiative transfer formulations. The clumping at the landscape scale is partly addressed via mechanism based on radiative transfer theory of canopy spectral invariants (Knyazikhin et al., 1998; Tian et al., 2002; Huang et al., 2007).

FAPAR product corresponds to the instantaneous value at the time of the satellite overpass. LAI and FAPAR are first produced daily. Then, the 8 days composite corresponds to the values of the product when the maximum FAPAR value within the eight days period is observed. Note that no LAI and FAPAR values are retrieved over bare and very sparsely vegetated area, permanent ice and snow area, permanent wetland, urban and water bodies.

For TERRA MODIS collection 5,

QA=0 indiates that the data were retrieved using the main algorithm without saturation, 

QA=1 indicates that the data were retrieved using the main algorithm with saturation, QA=2 indicates that the data were retrieved using the back-up algorithm while a QA of 3 indicated that no data was retrieved.

TERRA MODIS Collection 5 BELMANIP2 and DIRECT site extraction was performed at EMMAH, INRA. As the original MODIS projection is sinusoidal, the products were re-projected and extracted in Plate carrée (1/112° resolution) using the MODIS Reprojection Tool. A value of 0 was set for LAi and FAPAR over pixels identified as bare and very sparsely vegetated area.

 

REFERENCES

Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Nino, F., Weiss, M., Samain, O., Roujean, J.L. and Leroy, M., 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm. Remote Sensing of Environment, 110(3): 275-286.[URL]

Baret, F., Weiss, M., Lacaze, R., Camacho-decoca, F., Pacholcyzk, P., & Smets, B. (2010). Consistent and accurate LAI, FAPAR and FCOVER global products: principles and evaluation of GEOLAND2 products. In J. Sobrino (Ed.), Third International Symposium on Recent Advances in Quantitative Remote Sensing. Torrent (Spain)

Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P., & Smets, B. (2012). GEOV1: LAI, FAPAR Essential Climate Variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment, submitted
 

Bartholomé, E. and Belward, A., 2005. GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens., 26: 1959-1977 [URL]

Chen, J.M. and Leblanc, S.G., 1997. A four-scale bidirectional reflectance model based on canopy architecture. IEEE Trans. Geosci. Remote Sens., 35(5): 1316-1337 [URL]

Chen, J. M., C. H. Menges, et al. (2005). "Global mapping of foliage clumping index using multi-angular satellite data." Remote Sensing of Environment 97(4): 447-457. [URL]

Chen, J.M., Deng, F. and Chen, M., 2006. Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter. IEEE Geosccience and remote sensing, 44(8): 2230-2238 [URL]

Deng, F., Chen, J.M., Plummer, S., Chen, M. and Pisek, J., 2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Trans. Geosc. Remote Sens., 44(8): 2219-2229 [URL]

Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D., Strahler, A.H., Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F. and Schaaf, C., 2002. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, 83(1-2): 287-302.[URL]

Gobron, N., Pinty, B., Verstraete, M. and Govaerts, Y., 1999. MERIS Global Vegetation Index (MGVI): description and preliminary application. International Journal of Remote Sensing, 20(9): 1917-1927.[URL]

Gobron, N., 2011. Envisat's Medium Resolution Imaging Spectrometer (MERIS) Algorithm Theoretical Basis Document: FAPAR and Rectified Channels over Terrestrial Surfaces, Joint Research Center, Italy.[URL]

Hagolle, O., Lobo, A., Maisongrande, P., Cabot, F., Duchemin, B. and De Pereyra, A., 2005. Quality assessment and improvement of temporally composited products of remotely sensed imagery by combination of VEGETATION 1 and 2 images. Remote Sensing of Environment, 94(2): 172-186.[URL]

Huang, D., Knyazikhin, Y., Dickinson, R.E., Rautiainen, M., Stenberg, P., Disney, M., Lewis, P., Cescatti, A., Tian, Y., Verhoef, W., Martonchik, J.V. and Myneni, R.B., 2007. Canopy spectral invariants for remote sensing and model applications. Remote Sensing of Environment, 106(1): 106-122.[URL]

Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., François, C. and Ustin, S.L., 2009. PROSPECT + SAIL models: A review of use for vegetation characterization. Remote Sensing of Environment, 113(Supplement 1): S56-S66.[URL]

Knyazikhin, Y., Martonchik, J.V., Myneni, R.B., Diner, D.J. and Running, S.W., 1998. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J. Geophys. Res., 103(D24): 32257-32275.[URL]

Myneni, R.B., Hoffman, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y. and Smith, G.R., 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83(1-2): 214-231.[URL]

Roujean, J.L., M.Leroy and Deschamps, P.Y., 1992. A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data. J. Geophys. Res., 97(D18): 20455-20468 [URL]

Tian, Y., Woodcock, C.E., Wang, Y., Privette, J.L., Shabanov, N.V., Zhou, L., Zhang, Y., Buermann, W., Dong, J., Veikkanen, B. et al., 2002. Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty assessment. Remote Sensing of Environment, 83(3): 414-430.[URL]

Verhoef, W., 1984. Light scattering by leaf layers with application to canopy reflectance modeling : the SAIL Model. Remote Sens. Environ., 16: 125-141 [URL]

Verhoef, W., 1985. Earth observation modeling based on layer scattering matrices. Remote Sens. Environ., 17: 165-178 [URL]

Vermote, E.F., Tanré, D., Deuzé, J.L., Herman, M. and Morcrette, J.J., 1997. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Trans. Geosc. Remote Sens., 35(3): 675-686 [URL]

Weiss, M., Baret, F., Garrigues, S. and Lacaze, R., 2007. LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products. Remote Sensing of Environment, 110(3): 317-331. [URL]

Yang, W., Tan, B., Huang, D., Rautiniainen, M., Shabanov, N.V., Wang, Y., Privette, J.L., Huemmrich, K.E., Fensholt, R., Sandholt, I. et al., 2006. MODIS leaf area index products: from validation to algorithm improvement. IEEE Trans. Geosc. Remote Sens., 44(7): 1885-1898 [URL]