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Gio Global Land Component - Lot I
”Operation of the Global Land Component” Framework Service Contract N° 388533 (JRC)
PRODUCT USER MANUAL
FRACTION OF VEGETATION COVER (FCOVER) – VERSION 1
Issue I2.20
Organization name of lead contractor for this deliverable: VITO
Book Captain: Bruno Smets
Contributing Authors: Roselyne Lacaze
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Dissemination Level PU Public X
PP Restricted to other programme participants (including the Commission Services)
RE Restricted to a group specified by the consortium (including the Commission Services)
CO Confidential, only for members of the consortium (including the Commission Services)
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Document Release Sheet
Book captain: Bruno Smets Sign
Date 23.03.2015
Approval: Roselyne Lacaze Sign
Date 23.03.2015
Endorsement: Michael Cherlet Sign Date 02.04.2015
Distribution: Public
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Change Record
Issue/Rev Date Page(s) Description of Change Release
28.03.2013 All
Initial version from FP7/geoland2 document,
Issue I1.31, 05.03.2012. Based upon
SPOT/VGT input data
I1.00
I1.00 23.04.2014 All Update to PROBA-V input data I2.00
I2.00 25.08.2014 All Clarifications and editorial changes after
external review; update of the validation results I2.10
I2.10 23.03.2014 33-37 Include conclusions of the full quality
assessment over one year of products I2.20
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TABLE OF CONTENTS
1 Background of the document ................................................................................................ 9
1.1 Executive Summary .................................................................................................................... 9
1.2 Scope and Objectives ................................................................................................................. 9
1.3 Content of the document ........................................................................................................... 9
1.4 Related documents .................................................................................................................. 10
1.4.1 Applicable documents ................................................................................................................................ 10
1.4.2 Input ............................................................................................................................................................ 10
2 Review of Users Requirements ............................................................................................ 11
3 Algorithm ............................................................................................................................ 13
3.1 Definition of the variable ......................................................................................................... 13
3.2 Outline ..................................................................................................................................... 13
3.3 The retrieval Methodology ....................................................................................................... 14
3.3.1 Input data ................................................................................................................................................... 14
3.3.2 Pre-processing steps ................................................................................................................................... 16
3.3.3 Biophysical retrieval methodology ............................................................................................................. 18
3.4 Limitations of the Product ........................................................................................................ 22
4 Product Description ............................................................................................................. 23
4.1 File Format ............................................................................................................................... 23
4.2 Product Content ....................................................................................................................... 25
4.2.1 Data File ...................................................................................................................................................... 25
4.2.2 Quicklook .................................................................................................................................................... 27
4.3 Product Characteristics ............................................................................................................ 27
4.3.1 Projection and Grid Information ................................................................................................................. 27
4.3.2 Spatial information ..................................................................................................................................... 28
4.3.3 Temporal Information ................................................................................................................................. 28
4.3.4 Data Policies ................................................................................................................................................ 29
4.3.5 Contacts ...................................................................................................................................................... 29
5 Validation Results ............................................................................................................... 30
6 References ........................................................................................................................... 35
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List of Figures
Figure 1: Main steps of the retrieval algorithm applied to PROBA-V VGT-like TOA reflectances to
get the biophysical variable (FCover). ..................................................................................... 14
Figure 2: PROBA VGT-like P segments contents ........................................................................... 16
Figure 3: Flow chart showing the principle of the biophysical retrieval methodology. ..................... 19
Figure 4: Cumulated frequency computed over the BELMANIP2 sites for FCover during the period
2003-2005 for the original CYCLOPES V3.1 products (dashed line) and after the „best
available product“ obtained by scaling CYCLOPES V3.1. ....................................................... 20
Figure 5: Neural network architecture used to estimate FCover from TOC reflectances and
illumination geometry. .............................................................................................................. 21
Figure 6: Theoretical performances of FCover neural network (presented as boxplots with the solid
line corresponding to the median and the gray levels containing respectively 50%, 90% and
99% of the population). ............................................................................................................ 21
Figure 7: Regions of FCover product. ............................................................................................. 24
Figure 8: The cut-out of tiles ........................................................................................................... 28
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List of Tables
Table 1: Spectral characteristics of VGT-2 and PROBA-V sensors ............................................... 15
Table 2: The global FCover products available .............................................................................. 19
Table 3: Definition of the continental tiles. ...................................................................................... 24
Table 4: Range of values and scaling factors of FCover and FCover uncertainty. ......................... 25
Table 5: Description of the quality flag of FCover. .......................................................................... 26
Table 6: Description of HDF-5 file attributes ................................................................................... 26
Table 7: Description of HDF-5 layer attributes ................................................................................ 27
Table 8: Color coding for quicklook FCover images ....................................................................... 27
Table 9: Summary of Product Evaluation (PROBA-V GEOV1). The plus (minus) symbol means
that the product has a good (poor) performance according to this criterion. ........................... 33
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List of Acronyms
ATBD Algorithm Theoretical Basis Document
BRDF Bidirectional Reflectance Distribution Function
CEOS Committee on Earth Observation Satellite
DBF Deciduous Broadleaf Forest
EBF Evergreen Broadleaf Forest
FAPAR Fraction of Absorbed Photosynthetically Active Radiation
GEOV1 Products including LAI, FAPAR, FCover Version 1
GCOS Global Climate Observing System
GIO GMES Initial Operation
GL Global Land
HDF Hierarchical Data Format
LAI Leaf Area Index
LPV Land Product Validation Subgroup of CEOS
MODIS Moderate Resolution Imaging Spectroradiometer
MODC5 MODIS vegetation products Collection 5
MVC Maximum Value Composite
NCEP National Centres for Environmental Prediction
NLF Needle Leaf Forest
NDVI Normalized Difference Vegetation Index
NIR Near Infrared
NMOD Number of valid observations used for the normalisation
PROBA-V VEGETATION sensor on PRoject for OnBoard Autonomy platform
PUM Product User Manual
QFLAG Quality Flag
RMSE Root Mean Square Error
SVP Service Validation Plan
SWIR Short Wave Infrared
TOA Top Of Atmosphere
TOC Top Of Canopy
VGT VEGETATION sensor onboard SPOT satellites
VNIR Visible and Near Infrared
WGS World Geodetic System
ZIP Compressed archive file format
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1 BACKGROUND OF THE DOCUMENT
1.1 EXECUTIVE SUMMARY
The Global Land (GL) Component in the framework of GMES Initial Operations (GIO) is earmarked
as a component of the Land service to operate “a multi-purpose service component” that will
provide a series of bio-geophysical products on the status and evolution of land surface at global
scale. Production and delivery of the parameters are to take place in a timely manner and are
complemented by the constitution of long term time series.
The Product User Manual (PUM) is a self-contained document which gathers all necessary
information to use the product on an efficient and reliable way.
This document describes the GL Fraction of Vegetation Cover (FCover) Version 1 products,
derived from PROBA-V daily data. The FCover product is a 30-days composite, updated every 10
days using a moving window. The product is projected on a regular latitude/longitude grid with a
resolution of 1/112° (approx. 1km at the equator). It is delivered in 10°x10° tiles and in continental
tiles covering the whole globe (from 180°E to 180°W and from 75°N to 60°S). It is provided in multi-
band HDF5 format (by default format) containing the fraction of vegetation cover value, its
uncertainty, a quality flag and other additional information.
Note: The PROBA-V FCover V1 is disseminated as a “Demonstration” product, meaning that
some limitations still exist. Indeed the quality assessment of the PROBA-V normalized TOC
reflectances (TOC-r products), which are the input of neural networks to calculate the GEOV1 LAI,
FAPAR, FCover variables, is in progress. However, a preliminary analysis has showed systematic
positive differences between PROBA-V and SPOT/VGT TOC-r in near infrared and SWIR spectral
bands (PROBA-V TOC-r slightly higher than SPOT/VGT TOC-r). The reasons of such differences
are under investigation. Once fixed, a re-processing action of TOC-r and, then, of GEOV1 LAI,
FAPAR, FCover will be performed and a new quality assessment.
1.2 SCOPE AND OBJECTIVES
The PUM is the primary document that users have to read before handling the products.
It gives an overview of the product characteristics, in terms of algorithm, technical characteristics,
and main validation results.
1.3 CONTENT OF THE DOCUMENT
This document is structured as follows:
Chapter 2 recalls the users requirements
Chapter 3 presents a description of the algorithm.
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Chapter 4 describes the technical characteristics of the product.
Chapter 5 summarizes the validation procedure and the results.
1.4 RELATED DOCUMENTS
1.4.1 Applicable documents
AD1: Annex II – Tender Specifications to Contract Notice 2012/S 129-213277 of 7th July 2012
AD2: Appendix 1 – Product and Service Detailed Technical requirements to Annex II to Contract
Notice 2012/S 129-213277 of 7th July 2012
1.4.2 Input
Document ID Descriptor
GIOGL1_SSD Service Specifications Document of the Copernicus
Global Land Service.
GIOGL1_SVP Service Validation Plan of the Copernicus Global
Land service.
GIOGL1_ATBD_FCOVERV1 Algorithm Theoretical Basis Document of the
FCOVER Version 1 product
GIOGL1_ATBD_PROBA2VGT ATBD of the pre-processing module applied to
transform PROBA-V data in SPOT/VGT-like data
GIOGL1_ATBD_TOCRef ATBD of PROBA-V TOC reflectances
GIOGL1_PUM_TOCRef Product User Manual of the PROBA-V TOC
reflectances, Issue I2.00
GIOGL1_QAR-PROBAV-GEOV1 Quality Assessment Report of the PROBA-V GEOV1
(LAI, FAPAR, FCover Version1) products
1.4.3 External document
PROBAV-PUM Product User Manual of PROBA-V products,
available at http://proba-v.vgt.vito.be
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2 REVIEW OF USERS REQUIREMENTS
According to the applicable document [AD2], the user’s requirements relevant for Fraction of
Vegetation Cover are:
Definition:
o Fractional cover refers to the proportion of a ground surface that is covered by
vegetation.
Geometric properties:
o location accuracy shall be 1/3rd of the at-nadir instantaneous field of view
o pixel co-coordinates shall be given for centre of pixel
Geographical coverage:
o Geographic projection: regular lat-long
o Geodetical datum: WGS84
o Pixel size and accuracy: 1/112°; accuracy: minimum 10 digits
o Coordinate position: centre of pixel
o Window coordinates:
Upper Left:180°W-74°N
Bottom Right: 180°E 56°S
Frequency and timeliness:
o As a baseline the biophysical parameters are computed by and representative of
dekad, i.e. for ten-day periods (“dekad”) defined as follows: days 1 to 10, days 11 to
20 and days 21 to end of month for each month of the year.
o As a trade-off between timeliness and removal of atmosphere-induced noise in
data, the time integration period may be extended to up to two dekads for output
data that will be asked in addition to or in replacement of the baseline based output
data.
o The output data shall be delivered in a timely manner, I. e. within 3 days after the
end of each dekad.
Ancillary information:
o the number of measurements per pixel used to generate the synthesis product
o the per-pixel date of the individual measurements or the start-end dates of the
period actually covered
o quality indicators, with explicit per-pixel identification of the cause of anomalous
parameter result
Accuracy requirements: wherever applicable the bio-geophysical parameters should meet
the internationally agreed accuracy standards laid down in document “Systematic
Observation Requirements for Satellite-Based Products for Climate”. Supplemental details
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to the satellite based component of the “Implementation Plan for the Global Observing
System for Climate in Support of the UNFCCC”. GCOS-#154, 2011”.
As the FCover is not considered as an Essential Climate Variable, no specific accuracy
requirement is specified in the above document.
The above requirements are indicative. They have to be adapted and clarified by the User Board of
the Global Land service.
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3 ALGORITHM
The algorithm has been defined by INRA (Institut National de REcherche Agronomique) in the
framework of the FP7/geoland2 project. It generates the fraction of vegetation cover (FCover)
associated with the leaf area index (LAI) and the Fraction of absorbed PAR (FAPAR). These
products are known as the GEOV1 products (Baret et al., 2013).
3.1 DEFINITION OF THE VARIABLE
FCover corresponds to the gap fraction for nadir direction. It is used to separate vegetation and soil
in energy balance processes, including temperature and evapotranspiration. It is computed from
the leaf area index and other canopy structural variables and does not depend on variables such
as the geometry of illumination as compared to FAPAR. For this reason, it is a very good candidate
for the replacement of classical vegetation indices for the monitoring of green vegetation. Because
of the linear relationship with radiometric signal, FCover will be only marginally scale dependent
(Weiss et al., 2000). Note that similarly to LAI and FAPAR, only the green elements will be
considered, either belonging both to the overstorey and understorey.
The Copernicus Global Land FCover Version 1 product is derived from CYCLOPES V3.1 (Baret et
al., 2007) products.
3.2 OUTLINE
The PROBA-V FCover Version 1 is derived from the SPOT/VGT-like Top of Atmosphere (TOA)
PROBA-V reflectances generated by the PROBA2VGT module [GIOGL1_ATBD_PROBA2VGT].
The retrieval methodology is described in Baret et al., (2013) and summarized in Figure 1. The
TOA reflectances are pre-processed to obtain normalized Top of Canopy (TOC) reflectances,
according to the methodology applied to get the PROBA-V TOC-R products
[GIOGL1_ATBD_TOCRef and GIOGL1_PUM_TOCRef]. Then, the method to get the FCover from
TOC reflectances is based upon a neural network.
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Figure 1: Main steps of the retrieval algorithm applied to PROBA-V VGT-like TOA reflectances to get
the biophysical variable (FCover).
3.3 THE RETRIEVAL METHODOLOGY
3.3.1 Input data
The input data of the FCover Version 1 processing line are the SPOT/VGT-like Top of Atmosphere
(TOA) PROBA-V reflectance products generated by the PROBA2VGT pre-processor
[GIOGL1_ATBD_PROBA2VGT].
3.3.1.1 PROBA-V instrument
Since May 2013, a new VEGETATION sensor is operational on board of a PROBA2 platform, also
known as PROBA-V. Although the platform is designed to provide continuity to the SPOT-
VEGETATION sensor, it has been designed to provide an increase in resolution from 1km to 1/3
km.
To achieve this increase in resolution, the PROBA-V instrument is composed of three cameras.
Each camera is composed of 2 sensors, the VNIR sensor and the SWIR sensor. The VNIR sensor
provides through 3 detectors from one strip each, the blue, red and NIR bands. The SWIR sensor
provides through a single detector, built up from three strips, the SWIR band. Each camera can be
individually switched on/off. The NIR band observes the Earth first, followed by the RED, BLUE,
and SWIR bands. As a result, an observation time difference of 12s exists between the NIR and
SWIR bands.
An image consists of one or more [image] bands, each consisting of a given number of columns
and rows (the pixels). A PROBA-V segment covers a swath of about 500 km in case of the centre
camera, and a swath of about 700 km in case of the left / right camera. With a Field Of View of
102.4°, it reaches a swath of 2295 km, flying at an altitude of 820 km with an equatorial pass-over
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time between 10:30 and 11:30. The current overpass time is 10:45. More details are given in the
PROBA-V Product User Manual [PROBAV-PUM].
To compose the 1 km pixels, a compositing operation of the different cameras is performed.
Different cameras (left, center, right) provide images for different segments. The segments are
then composited into 1 image by using a Maximum Value Composite (MVC) (Tarpley et al. 1984,
Holben, 1986) approach on NDVI to provide the end user products. Details on the PROBA-V 1km
data are given in the ATBD of the pre-processing module [GIOGL1_ATBD_PROBA2VGT].
The spectral characteristics of PROBA-V are close, but different, to those of SPOT/VGT-2 (Table
1).
Spectral band SPOT/VGT-2 PROBA-V
Centre (nm) Width (nm) Wavelength (nm)
B0 (blue) 450 40 447 – 493
B2 (red) 645 70 610 – 690
B3 (NIR) 835 110 770 – 893
SWIR 1665 170 1 570 – 1 650
Table 1: Spectral characteristics of VGT-2 and PROBA-V sensors
3.3.1.2 PROBA VGT-like data
In order to re-use the existing processing chain ingesting SPOT-VGT P segments, a module has
been developed to transform the daily PROBA-V TOA tiles data into files compatible with the daily
SPOT-VGT P segments. This module performs:
1. Radiometric correction to keep the reflectance values consistent with the SPOT-
VGT time-series.
2. Format transformation to enable the re-use of TOC-r operational workflow.
3. Integration of meteorological ancillary data in TOA products.
All three operations are described in detail in the ATBD [GIOGL1-ATBD-PROBA2VGT].
The resulting products are SPOT-VGT compatible products. Every output product is a zip file
containing a set of files as shown in Figure 1. In addition, the SPOT-VGT compatible product,
representing Top-Of-Atmosphere data, contains two ancillary meteorological data files,
representing the water vapour and the ozone, in the ZIP contents.
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Figure 2: PROBA VGT-like P segments contents
3.3.2 Pre-processing steps
Before the ingestion into the neural network, the TOA reflectances are first pre-processed to get
normalize TOC reflectances.
3.3.2.1 Radiometric calibration
As the PROBA-V VGT-like input TOA reflectances are compatible with the SPOT/VGT-2 TOA
reflectances, the same calibration coefficients are used.
3.3.2.2 Cloud screening
Hagolle et al. (2004) developed a cloud screening method based on surface reflectance in the blue
band (B0) computed using the SMAC code (Rahman and Dedieu, 1994) and corrected only from
Rayleigh scattering and gaseous absorption. The 1 km spatial resolution GLC2000 global land
cover classification (Bartholomé and Belward, 2005) was used to drive the threshold values used
in blue band (B0). For barren, sparse herbaceous, artificial surfaces and associated areas, mosaic
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cropland/shrub cover, and shrub cover classes were tuned: the pixel is flagged as cloudy if (TOC-
r(B0)> 0.24) and (TOC-NDVI < 0.05). For the other classes, the original algorithm of Hagolle et al.
(2004) is applied. To separate snow from clouds a snow detection algorithm was finally
implemented based on spectral variation of snow (Dozier, 1989). Thresholds associated to each of
the four bands were empirically tuned. Note that cloud detection is not active over salt lakes. Cloud
screening was validated at global scale during April 1999 by comparison to quasi-simultaneous
cloud fraction observations observed over 11500 synoptic nebulosity ground stations provided by
Météo-France (Berthelot et al., 2004). Comparison with FP7/CYCLOPES project cloud mask
shows good performances over Europe, with however some over detection above snow, and under
detection in tropical regions, due to the difficulty to identify semi transparent clouds.
Once cloudy pixels are identified, cloud shadows are first projected over the surface taking into
account view and sun directions, assuming that clouds are at a 3 km altitude. The cloud mask is
spatially extended over a 2 x 2 pixels radius to prevent small contamination of clear pixels in the
cloud vicinity. Pixels are then declared ‘suspect’ over a 3 x 3 radius centered on the cloud detected
pixels. Note that cloudy pixels are not spatially extended over snowy pixels. Each valid pixel (not
cloudy, not shadowed, not saturated or not out of orbit) may thus be declared either clear (all the
bands valid), suspect (in the vicinity of cloudy/shadowed pixels), or snowy (with all the bands valid
or with just B0 and/or B2 (red) bands invalid).
3.3.2.3 Atmospheric correction
Atmospheric effects are corrected using the SMAC code (Rahman and Dedieu, 1994) for the four
spectral bands. Input atmospheric characteristics are derived from currently available products:
NCEP Meteorological data for pressure and water vapour, and TOMS/TOVS for ozone
(ftp://oceans.gsfc.nasa.gov). The aerosol optical thickness was derived from the minimum of
monthly values of MODIS aerosol products (MOD08_M3) (Kaufman et al., 1997). A climatology of
these values was built over the period covering March 2000 up to December 2006. However,
MODIS aerosol products present missing values and unexpected large AOT550 values
(AOT550>0.6) that were removed, mostly corresponding to transition between deserts and
vegetated areas. The gaps were thus filled with the simple latitudinal gradient proposed by
Berthelot and Dedieu (Berthelot and Dedieu, 1997). A spatial smoothing procedure is finally
applied to minimize the possible discontinuities between 1°× 1° pixels and to reach the l 1/112°
spatial resolution.
3.3.2.4 Normalization and compositing
The linear reflectance model of Roujean et al. (1992) is inverted to normalize the surface
reflectances and remove the bidirectional effects due to changes in solar and viewing angular
configurations during the synthesis period of 30 days. A Gaussian weighting function is applied to
the observations of the synthesis period: the maximum weight is put on the last observation of the
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period since these products are near real time. This model describes the bidirectional reflectance
as a linear combination of three geometrical kernels (fi), weighted by three coefficients. The
compositing method is improved using prior information (Hagolle et al., 2004): a confidence interval
for the coefficients of the Roujean model is used to constrain the least-squares fit. The constrained
BRDF model is finally used to detect iteratively and discard dates with residual cloud contamination
or atmospheric effects. The current algorithm is applied at a 10-day frequency on surface
reflectances acquired during a moving temporal window of 30 days to generate directional
parameters for each spectral band. Normalized reflectances are then computed with a solar zenith
angle corresponding to the median value of observed during the compositing period and a viewing
observation at nadir.
More details can be found in the ATBD [GIOGL1_ATBD_TOCRef] or in the PUM
[GIOGL1_PUM_TOCRef] of the PROBA-V TOC-Ref product.
3.3.3 Biophysical retrieval methodology
3.3.3.1 Principle of the algorithm
The biophysical algorithm (common to LAI, FAPAR and FCover Version 1) is based on already
existing ones to capitalize on the efforts accomplished and get a larger consensus from the user
community. Figure 3 shows the several steps used for each of these variables. Following the
published literature on products validation (Garrigues et al., 2008; Weiss et al., 2007), the best
performing existing products were selected and combined to take advantage of their specific
performances while limiting the situations where products show deficiencies. The selected
products are re-projected onto the SPOT/VGT plate-carrée 1/112° grid, smoothed through time
and interpolated at the 10 days frequency. Then the products are combined and eventually scaled
to compute the fused product that is expected to provide globally the ‘best’ performances. The
fused products are generated for few years over the BELMANIP2 set of sites that is supposed to
represent the possible range of surface types and conditions over the Earth (Baret et al., 2006).
Neural networks are then calibrated over this set of sites to relate the fused products to the
corresponding SPOT/VGT normalized TOC reflectances from the FP7/CYCLOPES project (Baret
et al., 2007).
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Figure 3: Flow chart showing the principle of the biophysical retrieval methodology.
3.3.3.2 Selection of the products used to generate the “fused products”
Only one global FCover product was available: the CYCLOPES dataset (Table 4)
Products FCover Temporal frequency
(days)
Time period
Projection Reference
CYCLOPES V3 10 1999-2007 Lat-lon (Baret et al., 2007)
Table 2: The global FCover products available
Existing Products
Selection
Product 1 Product 3
Fusion
FusedProduct
CYCLOPESL3a
Neural NetCalibration NNT Coefs
CYCLOPESL3a
Neural NetCalibration
GEOV1Product
AlgorithmCalibration
Application
SmoothingInterpolation
SmoothingInterpolation
Scaling
CYCLOPES Normalized TOC
reflectancesPROBA-V TOC-
Ref product
Neural Net
PROBA-V FCoverV1 product
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3.3.3.3 Product fusion
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 (Verger, 2008). This was corrected for by
applying a scaling factor to the CYCLOPES V3.1 products ( ). This factor (
was
corresponding to the inverse of the FCover value for the 99% cumulated frequency (Figure 4) that
should be expected to be very close to 1.0 since it should correspond to very dense canopies:
Where is the value that will be used for calibrating the neural network.
Figure 4: Cumulated frequency computed over the BELMANIP2 sites for FCover during the period
2003-2005 for the original CYCLOPES V3.1 products (dashed line) and after the „best available
product“ obtained by scaling CYCLOPES V3.1.
3.3.3.4 Calibration of the neural network
The scaled FCover product was used to calibrate neural network using as inputs the normalized
TOC reflectance in the three SPOT/VGT bands (red, near infrared and short wave infrared) along
with the cosine of the sun zenith angle corresponding to 10:00 local time (§ 3.2.2.4) (Figure 5).
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Figure 5: Neural network architecture used to estimate FCover from TOC reflectances and
illumination geometry.
The learning data set available was split into a set of cases containing 90% of the available data
while the 10% remaining part was used for hyper-specialization and theoretical evaluation (test
data set).
The theoretical performances of the network were evaluated over the test data set. Results show
that the training of the network was quite efficient, with relatively small RMSE value. The algorithm
is unbiased as expected (Figure 6).
Figure 6: Theoretical performances of FCover neural network (presented as boxplots with the solid
line corresponding to the median and the gray levels containing respectively 50%, 90% and 99% of
the population).
3.3.3.5 Quality flags and uncertainties
In addition to the quality flags used to describe TOC-Ref products, additional information is also
provided for FCover product:
Input out of range. This represents the consistency of the measured PROBA-V input
reflectances with those used in the training data base. A flag is raised when observations
are outside the training definition domain.
B2
B3
SWIR
Norm
Norm
Norm
Norm
Norm S
S
S
S
S
L Var
cos(s)
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Output out of range. If the output is outside the expected range of the biophysical
variable, a flag is raised.
Estimated uncertainties: This represents the expected error expressed in RMSE between
the estimated and the actual biophysical values. As a first approximation, this can be
derived from the theoretical performances of the algorithm as evaluated over an
independent data set. A specific network is trained to relate the estimated uncertainties to
the input reflectance and observation geometry values.
3.4 LIMITATIONS OF THE PRODUCT
The main limitations of the product are related to:
The temporal resolution: the products are provided at a 10 days sampling interval.
However, they are built from 30 days composite of PROBA-V observations. The original
products used to calibrate the algorithm were also based either on 30 days temporal
resolution (CYCLOPES) or 20 days (MODIS) 8 days within ±10 days.
Presence of water bodies or snow. The performances of the products in presence of
water bodies or snow (either pixels mixed with water bodies or snow, or water bodies or
snow representing a significant fraction of the background) are expected to be degraded.
This may be detected using the ‘input out of range’ flag.
Vegetated areas in the desert. Some known vegetated areas like oasis or irrigated crops
can appear like permanent “no data” pixels.
Time lag: Considering the compositing approach, and the delay necessary for processing,
there is, at final, a time lag of about 2 weeks between the representative date of the
products and its availability. This can be a limitation for NRT monitoring applications.
Validity domains are defined for the inputs (normalized spectral surface reflectances) of the neural
networks. If inputs are outside the validity domain, a flag is raised on the normalized reflectances.
Despite of that, the FCover is assessed by the neural network, and kept if it belongs to its physical
domain.
The neural network is calibrated with the FCover best estimates among existing products, and the
RMSE between predicted and actual Fcover values is about 0.037.
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4 PRODUCT DESCRIPTION
The PROBA-V FCover V1 product follows the following naming standard:
g2_BIOPAR_FCOVER_<YYYYMMDDHHMM>_<AREA>_<SENSOR>_V<Major.Minor>
where
<YYYYMMDDHHMM> gives the temporal location of the file. YYYY, MM, DD, HH, and MM
denote the year, the month, the day, the hour, and the minutes, respectively.
<AREA> gives the spatial coverage of the file. In our case, <AREA> is HxxVyy, the name of
the 10°x10° tile or the continent short name
<SENSOR> gives the name of the sensor used to retrieve the product, so PROBAV
<Major.Minor> gives the version number of the product. “Major” increases when the
algorithm is updated. “Minor” increases when bugs are fixed or when processing lines are
updated (metadata, color quicklook, etc...).
4.1 FILE FORMAT
The FCover products are distributed in zip archives. The archive contains following files:
multi-band HDF-5 format containing the following layers:
o FCOVER: Fraction of Vegetation Cover
o FCOVER-ERR: uncertainty on the variable
o FCOVER-QFLAG: quality flag
o NMOD: number of valid observations during the synthesis period
o LMK: land-sea mask based upon the GLC-2000 land cover map
a xml file containing the metadata conform to INSPIRE2.1. An xsl file allows displaying a
friendly view of the metadata file.
A quicklook in a coloured geo-tiff format. The quicklook sub-sampled to 25% in both
horizontal and vertical direction from the FCOVER layer.
A text file containing the copyright of the product.
Each file corresponds to a tile of 10° x 10°. A dedicated typology has been chosen to locate easily
each tile when reading its name. The globe is divided in 36 tiles in longitude and 18 in latitude
according to Figure 7. Only the green tiles, covering land, are generated and hence available. In
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winter season, the northern hemisphere has less observations, hence the number of available tiles
could be less. The products are also provided by continents (grouping of a set of tiles) as defined
in Figure 7, and named as in Table 3. The 6 colored rectangles, covering most land of our
continents, represent the available continents.
Figure 7: Regions of FCover product.
The Africa and South-America continent (AFRI and SOAM continental tiles in Table 3) are also
distributed through EUMETCast (see Section 4.3.5 for access information).
Short name Continent
AFRI Africa: 30°W – 60°E, 40°N – 40°S
ASIA Asia: 60°E – 180°E, 80°N – 0°N
EURO Europe: 30°W – 60°E, 80°N – 30°N
NOAM North America: 170°W – 50°W, 80°N – 10°N
OCEA Oceania: 100°E – 180°E, 0°S – 50°S
SOAM South America: 110°W – 30°W, 20°N – 60°S
Table 3: Definition of the continental tiles.
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4.2 PRODUCT CONTENT
4.2.1 Data File
The physical range of FCover and uncertainty of FCover are given in Table 4. The physical values
are retrieved by:
PhyVal = DN / ScalingFactor + Offset
where the scaling factor and the offset are given in the tables.
As an example, FCover is a byte for which a data value of 150 equals a physical value of 0.6.
FCover FCOVER-Err
Minimum physical value 0 0
Maximum physical value 1.0 0.2
Maximum DN value 250 40
Invalid 255 255
Out of Range by value superior to the max physical value 253 253
Out of Range by value inferior to the min physical value 254 254
Scaling factor 250 200
Offset 0 0
Table 4: Range of values and scaling factors of FCover and FCover uncertainty.
The parameter NMOD corresponds to the number of measurements used to process the FCover,
and hence reflects the number of valid observations during the synthesis period. The physical
values are equal to the digital number within the range [0, 60].
The parameter LMK is a land cover map based upon the GLC2000 product. It integrates the class
“salt lakes” at global scale. The digital numbers are the sum of 200 and of the class number of the
GCL2000 map.
The quality flag (QFLAG) must be read bit per bit (Table 5). Its description is given in the table
below: bit 15 and bit 16 have no meaning. The QFLAG is coded on 2 bytes with an invalid value
equal to 65535.
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Bit = 0 Bit = 1
Bit 1: Land/Sea Land Sea
Bit 2: Snow status Clear Snow
Bit 3: Suspect No suspect Suspect
Bit 4: Aerosol status Pure Mixed
Bit 5: Aerosol source Modis Climato
Bit 6: Input status OK Out of range or invalid
Bit 7: LAI status OK Out of range or invalid
Bit 8: fAPAR status OK Out of range or invalid
Bit 9: fCover status OK Out of range or invalid
Bit 10: B2 saturation status OK Saturated
Bit 11: B3 saturation status OK Saturated
Bit 12: Filtering status Not filtered Filtered
Bit 13: Gap filling status Not filled Filled
Bit 14: NDVI status OK Invalid
Table 5: Description of the quality flag of FCover.
The multi-band HDF-5 file contains a number of HDF-5 attributes on the file-level, see Table 6 and
on the layer-level, see Table 7.
Attribute name Attribute description
Archive_facility Name of archiving center
Centre Name of processing/dissemination center
Ellipsoid_name Name of ellipsoid system
Geodate_name Name of date system
LAT Lattitude (top left)
LONG Longitude (top left)
Instrument_ID Sensor name
Overall_quality_flag Quality indicator
Pixel_size Size of a single pixel (in square)
Product_time Time of product creation
Product_algorithm_version Version of algorithm during processing
Projection_name Name of projection system
Region_name Area name
Satellite Platform name
Table 6: Description of HDF-5 file attributes
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Attribute name Attribute description
Class Type of layer (DATA)
Nb_bytes Data type (e.g. uint8, uint16, …)
Order_bytes Order data type (1= intel)
Product Layer name
Missing_value Data number representing missing value
Offset Offset factor to retrieve physical value
Scaling_factor Scale factor to retrieve physical value
Table 7: Description of HDF-5 layer attributes
4.2.2 Quicklook
The quicklook is a geo-referenced tiff file. The spatial resolution is sub-sampled, using nearest
neighbour resampling, to 25% in both directions, hence a quicklook is 1/16th of the size of the data
layer.
The quicklook is coded in 1 byte, using the same offset and scale parameters as the data layer.
The quicklook is provided with an embedded color table (Table 8).
Table 8: Color coding for quicklook FCover images
4.3 PRODUCT CHARACTERISTICS
4.3.1 Projection and Grid Information
The product is displayed in a regular latitude/longitude grid (plate carrée) with the ellipsoïd WGS
1984 (Terrestrial radius=6378km). The resolution of the grid is 1/112° (approx. 1km at the equator).
The reference is the centre of the pixel. It means that the longitude of the upper left corner of the
pixel is (pixel_longitude – angular_resolution/2.)
Each tile covers an area of 10°x10°, while the continental tiles cover a multiple of these 10°x10°
tiles as defined in Table 3.
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Figure 8: The cut-out of tiles
Starting from the upper left center pixel and taking 10° or 1120 pixels into account, the final tile is
an array of 1121 lines and 1121 columns taking the bottom/right upper/left half pixel into account
(Figure 8). As such there is an overlap between the tiles. For example, the last column of tile
H18V4 is the first column of the tile H19V4, and the last line of H18V4 is the first line of the tile
H18V5.
Note that the same reasoning holds also for the continental tiles.
4.3.2 Spatial information
As shown in Figure 7, the FCover product is provided, hence from longitude 180°E to 180°W and
latitude 75°N to 60°S. Any areas cover latitude above 75°N and below 60°S is filled with no-values
(255 for Byte and 65535 for Uint16).
4.3.3 Temporal Information
The FCover products are a 30-days composite, updated every 10 days using a moving window.
The temporal information “YYYYMMDDHHMM” in the filename is the “actual” date corresponding
10° = 1120 pixels
1121 pixels = 10.0089°
10° = 1120 pixels
1121 pixels = 10.0089°
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to the product value: the sum of weights of the weighting function (see paragraph 3.3.2.4) is the
same on each side of this date.
For example, the file g2_BIOPAR_FCOVER_200408130000_H17V04_VGT_V1.1.h5 corresponds
to the synthesis period from 26/07/2004 to 25/08/2004. The day 13/08/2004 is the day where the
sum of weights of the weighting function is the same on each side of this date.
Important note: The “actual” date is the date to take in consideration for any analysis.
The temporal information is detailed in the metadata (xml file) by 2 fields "EX_TemporalExtent :
beginPosition" and "EX_TemporalExtent : endPosition" as “YYYY-MM-DDTHH:MM::SS” giving the
beginning and the end of the 30-days time period, respectively. The endPosition of the 30-days
time period is always set to the 05th, 15th or 25th day of the month.
4.3.4 Data Policies
Any use of the original PROBA-V FCover products implies the obligation to include in any
publication or communication using these products the following citation:
"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 product has
received funding from various European Commission Research and Technical Development
programmes. The product is based on PROBA-V 1km data ((c) ESA and distributed by VITO).”
The user accepts to inform the production and distribution center of their publications through the
following address: [email protected].
4.3.5 Access and Contacts
The PROBA-V FCover V1 product is available through the Copernicus Global Land service
website at the address: http://land.copernicus.eu/global/products/fcover
Accountable contact: European Commission Directorate – General Joint Research Center
Email address: [email protected]
Scientific contact: Institut National de la Recherche Agronomique (INRA), France
Email address: [email protected]
Production and distribution contact: VITO
Email address: [email protected]
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5 VALIDATION RESULTS
5.1 VALIDATION PROCEDURE
The quality assessment of the PROBA-V GEOV1 products (LAI, FAPAR, FCover) during the first
year of data was conducted following the validation procedure described in the Copernicus Global
Land Service Validation Plan [GIOGL1_SVP] in agreement with the CEOS LPV best practices for
validation of LAI products. This study was focused on the consistency of PROBA-V and
SPOT/VGT GEOV1 products for the 6-months overlap period (mid November 2013 – mid May
2014). Moreover, MODIS C5 products were considered for inter-comparisons during the overlap
period and for the whole 2014. The following main criteria were assessed: completeness, spatial
consistency, temporal consistency, smoothness, statistical assessment of discrepancies with
similar products, and accuracy.
5.2 MAIN RESULTS
Product completeness
PROBA-V GEOV1 products shows a slight higher fraction of missing values (around 5% in overall)
than the SPOT/VGT GEOV1 products. The spatial and temporal distribution of the missing values
was consistent with that of SPOT/VGT, except over northern latitudes and equatorial areas.
Missing values for Needle Leaf Forest (NLF) reach up to 80% in the European wintertime. The
larger discrepancies were found over the Evergreen Broadleaf Forest (EBF) type, showing
PROBA-V fraction of missing values up to 60%. Per continental region, the larger discrepancies
are observed in South America, where the contribution of EBF forest is very important.
The temporal length of missing values was very consistent between PROBA-V and SPOT/VGT
GEOV1 products, with MODIS C5 products showing shorter length of gaps.
Spatial consistency
PROBA-V GEOV1 maps and difference maps with SPOT/VGT GEOV1 shows a very good
consistency, with most of the values within the optimal levels of 0.5 for LAI and 0.05 for FAPAR
and FCOVER. The analysis of residuals over global data confirms the good spatial consistency
between PROBA-V and SPOT/VGT with more than 80% of data within optimal levels (up to 92%
for LAI). Nevertheless, a slight overestimation of PROBA-V FCover was detected mainly over EBF
biomes and the South America, which shows the highest vegetation cover values during this
period.
Differences between PROBA-V and MODIS show also large fraction of pixels within the optimal
levels for the LAI (>72%). Larger discrepancies were found in areas dominated by EBF or
Deciduous Broadleaf Forest (DBF) in South America, Africa and Asia. However, for the FAPAR,
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both products shows large spatial inconsistencies around the world for the whole 2014 year as
previously reported between SPOT/VGT and MODIS C5. MODIS provides larger FAPAR values
over semi-arid regions whereas PROBA-V provides larger FAPAR values over more vegetated
regions. Better agreement is found for densest equatorial forests.
The spatial variability in homogeneous zones shows very similar results between PROBA-V and
SPOT/VGT over three areas (forest, shrublands, herbaceous) studied. Very good repeatability
(spatial precision) is achieved. MODIS shows similar results for LAI but very low spatial variability
for FAPAR. Over a selected heterogeneous area, showing an important gradient of vegetation,
PROBA-V GEOV1 showed very good dynamic range, good consistency with SPOT/VGT and the
larger spatial variability.
Temporal consistency
PROBA-V temporal variations were found very consistent with that of SPOT/VGT products for the
different biomes, as well as with MODIS products. The cross-correlation between PROBA-V and
SPOT/VGT temporal variations was higher than 0.9 typically in more than 70% of the sites for the
different biomes with the exception of EBF types where much lower correlations were observed.
The cross-correlation with MODIS products was also satisfactory, with a range of 50-70% of the
samples with correlations better than 0.9.
GEOV1 temporal variations over two Deciduous Tropical Forest Enviro-Net sites
(http://www.enviro-net.org) were found consistent with ground data. However, PROBA-V FCover
shows unreliable seasonal patterns over some desertic sites (e.g. Sahara) as it was previously
reported for SPOT/VGT GEOV1 products.
Intra-Annual Precision (smoothness)
PROBA-V GEOV1 products shows a good intra-annual precision, almost identical to that of
SPOT/VGT equivalent product, and much better than MODIS.
Statistical Analysis of discrepancies
Scatter-plots and uncertainty statistics between GEOV1 (PROBA-V vs SPOT/VGT) products over
BELMANIP 2.1 sites show the optimal consistency between both products, with overall correlations
higher than 0.96, no mean bias, and overall discrepancies (RMSE=0.3, 0.03, 0.04 for LAI, FAPAR
and FCover respectively) lower than the GCOS requirements on accuracy for LAI and FAPAR.
For the LAI product, very little systematic differences were found, with some bias (higher values for
PROBA-V) for LAI values higher than 6. For the FAPAR, very good consistency was found with
median values very close to zero regardless the interval of FAPAR considered. For the FCover,
systematic differences between PROBA-V and SPOT/VGT are larger, with a slight but systematic
overestimation of PROBA-V for FCover values larger than 0.4. The different behavior of the LAI,
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FAPAR and FCover can be partly explained due to the different impact of the input variations as
each variable uses a different neuronal network.
The systematic and overall discrepancies between PROBA-V GEOV1 and MODIS C5 products are
larger and show a larger dynamic as a function of the GEOV1 product value. For the LAI, large
scattering was found for LAI between 3 and 5, and the bias can be larger than 2. For the FAPAR, a
clear tendency was observed in the systematic differences. MODIS provides larger values for the
lower FAPAR values (<0.3), as MODIS overestimates FAPAR for very low values (Camacho et al.,
2013). However, GEOV1 provides larger values than MODIS mainly for FAPAR larger than 0.5.
The discrepancies between PROBA-V GEOV1 and MODIS C5 are similar to that found between
SPOT/VGT and MODIS C5:
Analysis per biomes:
For the LAI, very good agreement between PROBA-V and SPOT/VGT was found for all biomes,
with no systematic discrepancies. For the FAPAR, the agreement is optimal for all biomes with no
systematic deviations. For the FCover, no bias was found for non-forest types, whereas for forest
biomes PROBA-V shows slightly higher values than SPOT/VGT. Larger discrepancies (mean bias
of 0.04) and overestimation was found in EBF type, where maximum values (FCover=1) were
generally observed.
Scatter-plots between PROBA-V and MODIS LAI products shows a large scattering for EBF type,
and higher LAI values for DBF, NLF and Cultivated sites were obtained with PROBA-V. For
Herbaceous and Shrubs/Sparse, the agreement is quite good with low bias. Regarding the
FAPAR, an asymmetric behavior in Cultivated, Herbaceous and DBF sites was observed with a
tendency of PROBA-V to overestimate the MODIS FAPAR estimates for large values, whereas
MODIS products overestimates PROBA-V for low values. This tendency was observed for all
biomes types except in EBF and NLF sites with larger FAPAR values.
Analysis per continental region
PROBA-V and SPOT/VGT products show very good agreement for all the continental regions with
almost no bias for all the regions and products. Only for the FCover in South America, a large
systematic overestimation was found as this region presents the highest vegetation values.
The comparison between PROBA-V and MODIS products shows much larger residuals and RMSE
values for all the regions. The LAI larger discrepancies were also found in South America, as large
discrepancies were found for EBF and Cropland types which are dominant classes in this
continent. For the FAPAR, a positive difference was observed in North America, South America
and Europe whereas a negative tendency was found in Oceania.
The specific analysis over Africa shows in general larger discrepancies among the PROBA-V
GEOV1, MODISC5 and LSA SAF products for the three variables. The discrepancies between
PROBA-V and MODIS or LSA SAF LAI or FAPAR are however better than discrepancies between
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the two operational satellite products used as references. The large discrepancies observed for the
FCover are partly explained by a systematic bias in the LSA SAF products for low vegetation
values.
Accuracy Assessment
The accuracy of PROBA-V GEOV1 LAI product was very close to GCOS requirement using limited
concomitant data (RMSE=0.54) or using additional non-concomitant references (RMSE=0.51). For
the FAPAR the accuracy is also quite good (0.11 for all data), but a slight overestimation was
observed mainly as compared to concomitant data (bias=0.09). The FCover shows the worst
performance, with a systematic positive bias observed for mainly for forest and cropland sites (up
to 0.15 for concomitant data) and overall error of 0.14. PROBA-V shows similar performances
than MODIS for both LAI and FAPAR products.
Concluding remark
The validation results of the PROBA-V GEOV1 products over one year (2014) period shows very
good spatial and consistency with the SPOT/VGT GEOV1 products for the overlap period.
However, a positive bias as compared to SPOT/VGT has been detected for the FCover mainly for
values larger than 0.5. This bias seems to be confirmed by the limited ground observations
available. All the criteria evaluated including accuracy assessment shows positive results (Table
9). The main drawback of the product is the completeness which is slightly lower than in
SPOT/VGT GEOV1 products.
Table 9: Summary of Product Evaluation (PROBA-V GEOV1). The plus (minus) symbol means that
the product has a good (poor) performance according to this criterion.
QA
Criteria Performance Comments
Product
Completeness -
Main limitations over Northern latitudes in wintertime and
Equatorial areas.
Spatial
Consistency +
Optimal spatial consistency between PROBA-V and SPOT/VGT
GEOV1 products. Most of the differences between both lies
within 0.5 for LAI and 0.05 for FAPAR/FCOVER
Good repeatability over well known homogenous areas (Dense
Forest and Shrublands).
Good variability for known spatial gradients.
Larger discrepancies between PROBA-V and MODIS over EBF
and DBF for LAI and globally for FAPAR (similar than
SPOT/VGT and MODIS).
Temporal
Consistency +
Good consistency of PROBA-V GEOV1 temporal variations, as
compared to SPOT/VGT GEOV1 and MODIS C5. Cross-
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correlations between PROBA-V and SPOT/VGT GEOV1 (higher
than 0.9 in more than 70% of the sites for most of biomes
except in EBF).
Realism of temporal profiles over deciduous forest. Unreliable
seasonality of the FCover over some desertic sites.
Intra-Annual
Precision + Very smooth temporal profiles
Statistical
Analysis of
Discrepancies
+
Optimal consistency between PROBA-V and SPOT/VGT
GEOV1 (RMSE=0.3, 0.03, 0.04 for LAI, FAPAR and FCover
over BELMANIP2.1
Larger discrepancies between both GEOV1 products and
MODIS C5.
FCover PROBA-V shows higher values than SPOT/VGT mainly
for forest sites.
Accuracy ± Good accuracy with limited ground dataset for LAI and FAPAR.
Positive bias for FCover.
The details of the analysis are reported in a Quality Assessment Report [GIOGL1_QAR_PROBA-
V-GEOV1] available on http://land.copernicus.eu/global/products/FCover.
Furthermore, the quality assessment of the PROBA-V normalized TOC reflectances (TOC-r
products), which are the input of neural networks to calculate the GEOV1 LAI, FAPAR, FCover
variables, is in progress. However, a preliminary analysis has showed systematic positive
differences between PROBA-V and SPOT/VGT TOC-r in near infrared and SWIR spectral bands
(PROBA-V TOC-r slightly higher than SPOT/VGT TOC-r). The reasons of such differences are
under investigation. Once fixed, a re-processing action of TOC-r and, then, of GEOV1 LAI, FAPAR,
FCover will be performed and a new quality assessment exercise will be carried out.
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