Download FAPAR - Food and Agriculture Organization of the United Nations

Development of Standards for Essential Climate Variables: Fraction of
Absorbed Photosynthetically Active Radiation (FAPAR)
FAPAR, sometimes also called fAPAR or fPAR, is a radiation measure which indicates the
presence of vegetation and reflects its photosynthetic activity. Spatially-detailed descriptions of
FAPAR provide information about the relative strength and location of terrestrial carbon pools
and fluxes. It is one of the surface parameters that may be used in quantifying the CO2
assimilation by plants and the release of water through evapotranspiration.
1.
Definitions and units
FAPAR is defined as the fraction of photosynthetically active radiation (PAR) absorbed by a
plant canopy. PAR is the solar radiation reaching the canopy in the wavelength region 0.400 –
0.700 micrometres. ‘Total’ FAPAR excludes the incident PAR reflected from the canopy and the
radiation absorbed by the soil surface (or the combination of forest floor and the vegetation layer
below the tree crowns, i.e. understory), but it includes the portion of PAR which is reflected by
the soil/understory and absorbed by the canopy before escaping back to the atmosphere.
‘Green’ FAPAR refers to the fraction absorbed only by green leaves. This radiation drives
photosynthetic activity by the photons providing energy for biochemical processes within leaf
cells. ‘Green’ FAPAR is lower than ‘total’ FAPAR because it does not include PAR absorption
by the supporting woody material (in forest) or by dead leaves (in crops). The ‘green’ FAPAR is
the variable of interest for climate change purposes, and is the quantity discussed below.
While FAPAR is based on an instantaneous measurement, for climate change applications
representative daily values are required. They are obtained through direct measurements, or by
assuming variation with the cosine of the solar zenith angle to obtain the daily ‘green’ FPAR. As
the ratio of two radiation quantities, FAPAR is a unitless variable.
2.
Existing in situ measurement methods and standards
FPAR in situ measurements are closely related to those required to determine leaf area index
(LAI). Possibly for this reason and because LAI measurements are more difficult, much of the
methodology development for measurements has focused on LAI.
FAPAR in situ determination requires simultaneous measurements of PAR above and within a
canopy. The latter may be taken at various heights, thus establishing a FAPAR profile within a
canopy. To circumvent the complications arising from the different geometries of direct and
diffuse sunlight, FAPAR measurements are typically taken under overcast conditions. The basic
measurement requires the use of pyranometers (WMO, 2006) but various commercial
instruments have been built which may used for in situ FAPAR measurements (e.g.,
1
Leblanc et al., 2005; http://www.licor.com/env/Products/Sensors/rad.jsp). The location of
measurement sites and the measurement protocols vary with the measurement objectives (e.g.,
Margolis, 1997) , and no single standard has been adopted.
Existing in situ FAPAR measurements appear limited. For example, when searching for data sets
suitable for validating satellite- derived products, Yang et al. (2006) found that most of the field
data are limited to LAI and only a few FPAR measurements; therefore, they did not attempt to
validate satellite- derived FAPAR products.
3.
Existing satellite measurement methods and standards
Existing satellite FAPAR products are typically based on the close relationship between FAPAR
and vegetation indices (VI, i.e. various ratios involving spectral reflectance in the infrared and
red parts of the electromagnetic spectrum). Since FAPAR has pronounced seasonal and diurnal
variations, the FAPAR retrieval algorithms incorporate these effects as well as the dependence
on cover type. The main strategies so far have been empirical VI- FAPAR relationships based on
field measurements typically in combination with higher resolution satellite images (e.g., Chen
and Cihlar, 1999), or on radiative transfer models (e.g., Myneni et al., 1997; Gobron et al. ??)
which are constrained by in situ measurements. These methods require cloud-free optical data,
thus measurements over several days are used to minimize cloud interference. At present, there
are two main sources of satellite- based FAPAR data derived from several optical sensors
(http://fapar.jrc.it/WWW/Home.php; http://modis-250m.nascom.nasa.gov/).
Calibration, validation and intercomparisons of FAPAR products from various sources are as
important for FAPAR as for other satellite- derived product types. Consistencies are established
by adjusting retrieval algorithms based on validation results (e.g., Yang et al., 2006), or by
optimizing these algorithms with the use of sensor- specific models (e.g., Rahman et al.,1993).
So far, the validation and intercomparisons of FAPAR products have been more limited than
those of LAI , with only one group (JRC; http://fapar.jrc.it/WWW/Home.php) having focused on
this aspect. As noted above, the lack of suitable in situ data sets has been a limitation to such
activities. Nevertheless, limited assessments have been undertaken (e.g., Fensholt et al., 2004;
Gobron et al., 2006). Comparisons with models have also been used to determine the realism of
the existing FAPAR products (Tian et al., 2004). Some validation and intercomparisons studies
are ongoing, but substantially less than for LAI which seems to have a priority in international
collaborative projects (e.g., Morisette, 2006; Garrigues et al., 2006). More systematic effort is
required for FAPAR (TOPC, 2004). No standards have been established for such studies;
however, the protocols employed in previous studies are described in the peer- reviewed
literature.
2
4.
Conclusions
1. Due to the spatial and temporal variability of FAPAR, the information required for climate
change purposes can only be obtained through measurements by satellite optical sensors.
2. Although the basic in situ measurements are well understood, the procedures and
measurement equipment differ depending on the available equipment and the measurement
team choices based on the objectives, in addition to the characteristics of the biome under
consideration and other factors.
3. While experimental FAPAR products have been generated from satellite data sources,
limited validation and intercomparisons of products have been conducted to date using ad
hoc experimental protocols; such activities could be usefully combined with those focused on
the leaf area index.
4. Although standardization of satellite- derived products is premature, there is strong need to
develop community- consensus procedures for the calibration and validation of FAPAR
products from individual satellite optical sensors, and for the intercomparisons of such
products from different sensors or missions.
5.
Recommendations
1. Consensus procedures for making in situ FAPAR measurements should be described that are
suitable for use with different instruments and in various biomes. These procedures should be
described in guides to be used by scientists and others making FAPAR in situ measurements.
2. Community consensus procedures for the calibration, validation and intercomparisons of
satellite- based FAPAR products should be documented and published.
3. As the satellite- based FAPAR products mature and their usefulness is established,
procedures should be defined and endorsed for the ongoing processing and transformation of
satellite optical measurements into FAPAR products.
6.
References
Chen, J. M., and J. Cihlar. 1999. BOREAS RSS-07 Regional LAI and FPAR Images From TenDay AVHRR-LAC Composites. Data set. Available on-line [http://www.daac.ornl.gov] from
Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee,
U.S.A.
Fensholt, R., Sandholt, I., and Rasmussen, M.S. 2004. Evaluation of MODIS LAI, fAPAR and
the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements.
Remote Sensing of Environment 91: 490-507.
Garrigues, S., Morisette, J., Nickeson, J., Lacaze, R., Fernandes, R., Yang, W., Myneni, R.,
Baret, F., Weiss, M., and Plummer, S. 2006. Leaf Area Index inter-comparison and validation
3
as a prototype activity for WGCV/LPV. Presented at the WGCV Plenary, 9-12 May, Budapest,
Hungary. Available at http://wgcv.ceos.org/docs/plenary/wgcv25/lpv_report_plenary25.pdf
(accessed 2007-02-22).
Gobron, N., Pinty, B., Aussedat, O., Chen, J., Cohen, W. B., Fensholt, R., Gond, V., Hummerich,
K. F., Lavergne, T., Melin, F., Privette, J. L., Sandholt, I., Taberner, M., Turner, D. P.,
Verstraete, M. M. and Widlowski, J.-L. 2006. Evaluation of FAPAR products for different
canopy radiation transfer regimes: Methodology and results using JRC products derived from
SeaWiFS against ground-based estimations. Journal of Geophysical Research 111 (D13110), doi:
10.1029/2005JD006511.
Leblanc, S.G., Chen, J.M., Fernandes, R., Deering, D.W., and Conley, A. 2005. Methodology
comparison for canopy structure parameters extraction from digital hemispherical photography
in boreal forests. Agricultural and Forest Meteorology 129: 187–207.
Margolis, H. 1997. Relationship between FPAR and leaf nitrogen for black spruce, jack pine and
aspen stands at the BOREAS Northern Study Area. Available at http://wwweosdis.ornl.gov/BOREAS/bhs/Documents/TE09_parnprof.html#Section%202 (accessed 200702-22).
Myneni, R. B., Nemani, R. R., and Running, S. W. 1997. Algorithm for the estimation of global
land cover, LAI and FPAR based on radiative transfer models. IEEE Transactions on Geoscience
and Remote Sensing 35: 1380– 1393.
Rahman, H., Verstraete, M., and Pinty, B. 1993. Coupled surface-atmosphere reflectance
(CSAR) model. 1: Model description and inversion on synthetic data . Journal of Geophysical
Research 98, (D11): 20,779-20,789.
Tian, Y., Dickinson, R. E., Zhou, L., Zeng, X., Dai, Y., Myneni, R. B., Knyazikhin, Y., Zhang,
X., Friedl, M., Yu, H., Wu, W., and Shaikh, M. 2004. Comparison of seasonal and spatial
variations of leaf area index and fraction of absorbed photosynthetically active radiation from
Moderate Resolution Imaging Spectroradiometer (MODIS) and Common Land. Journal of
Geophysical Research 109 (D01103), doi:10.1029/2003JD003777.
Morisette, J. 2006. CEOS Land Product Subgroup Report. Presented at the WGCV Plenary, 9-12
May, Budapest, Hungary. Available at
http://wgcv.ceos.org/docs/plenary/wgcv25/lpv_report_plenary25.pdf (accessed 2007-02-22).
TOPC. 2004. Terrestrial Observation Panel for Climate Report to WGCV 22. 22nd Meeting of
the Working Group on Calibration and Validation, 15th June, Sioux Falls, SD. Available at
http://wgcv.ceos.org/documentation/wgcv22.htm (accessed 2007-02-15).
Yang, W., Huang, D., Tan, B., Stroeve, J. C., Shabanov, N. V., Knyazikhin, Y., Nemani, R. R.,
and Myneni, R. B.. 2006. Analysis of leaf area index and fraction vegetation absorbed PAR
products from the Terra MODIS sensor: 2000–2004. IEEE Transactions for Geoscience and
Remote Sensing 44: 1829–1842.
4
WMO. 2006. Guide to Meteorological Instruments and Methods of Observation. Preliminary
seventh edition. Report WMO-No. 8, Geneva, Switzerland.
5