Hindawi Publishing Corporation
Journal of Sensors
Volume 2012, Article ID 582159, 11 pages
doi:10.1155/2012/582159
Review Article
Review of Available Products of Leaf Area Index and Their
Suitability over the Formerly Soviet Central Asia
M. W. Kappas and P. A. Propastin
Department of Geography, Georg-August University of Go¨ttingen, Goldschmidtstraße 5, 37077 Go¨ttingen, Germany
Correspondence should be addressed to M. W. Kappas, mkappas@gwdg.de
Received 7 June 2011; Revised 16 November 2011; Accepted 19 December 2011
Academic Editor: Tuan Guo
Copyright © 2012 M. W. Kappas and P. A. Propastin. This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
Leaf area index (LAI) is a key biophysical variable for environmental process modelling. Remotely sensed data have become the
primary source for estimation of LAI at the scales from local to global. A summary of existing LAI data sets and a discussion
of their appropriateness for the formerly Soviet Central Asia, especially Kazakhstan, which is known for its huge grassland area
(about 2 million km2), are valuable for environmental modelling in this region. The paper gives a brief review of existing global
LAI products, such as AVHRR LAI, MODIS LAI, and SPOT-VEGETATION LAI, and shows that validation of these products in
Kazakhstan as well as in other countries of the formerly Soviet Central Asia has not been carried out yet. Apart from the global
LAI products, there are just a few data sets retrieved by remote sensing methods at subregional and regional scales in Kazakhstan.
More research activities are needed to focus on the validation of the available global LAI products over the formerly Soviet Central
Asia and developing new LAI data sets suitable for application in environmental modelling at different scales in this region.
1. Introduction
Usually, the Leaf area index (LAI) is defined as one half the
total leaf area per unit ground surface area projected on the
local horizontal area (e.g., [1]). The LAI is well adapted for
flat leaves as, for example, grass, crops, and deciduous forests.
In coniferous woodland, shoot is considered as the foliage
element, and the assembly of needles (e.g., angle, shape)
should be taken into account (e.g., as clumping index). In
deciduous forests the measurements need to be corrected for
clumping index as well, but at scales larger than that of a
shoot.
The LAI is a significant ecological attribute that controls
vegetation photosynthetic activity. As such, LAI plays an
essential role in climate, weather, and ecological studies. In
the realm of possible climate change and its influence on
landscape’s future CO2 sequestration potential, more precise
knowledge about the theoretical production ecology of the
various world biomes (wetlands, woodlands, shrublands, or
grasslands) is essential. LAI is widely used as input variable
for land surface modelling of biosphere processes, and espe-
cially for predictions of photosynthetic primary production
[2, 3].
The LAI belongs to the biophysical variables which are
useful to the development of knowledge in climate and envi-
ronmental sciences, to understand the climatic system and
ecophysiological processes. The biophysical parameters are
also indispensable as input to environmental services that
use these data, at the same time as other data types (in situ,
agrometeorological models, etc.) to produce environment
monitoring indicators (water quality, drought or famine
risks, desertification, deforestation/reforestation, etc.).
Especially for the formerly Soviet Central Asia with its
huge grassland areas, the allocation of biophysical parame-
ters like LAI is more and more required. Despite of climate
aridity, the grassland ecosystems of Central Asia represent a
gigantic reservoir of carbon and play a very considerable role
in the global change [4, 5]. Both the climatic conditions and
physiographic patterns vary significantly within this region
presenting a broad spectrum of various landscapes within
2 Journal of Sensors
the grassland biome of Central Asia. Grasslands of Central
Asia distinguish considerably from grasslands in other re-
gions, making difficult adaptation of any global analysis
approach to the Central Asian region. The history of land use
change occurred in Central Asia (especially in Kazakhstan) in
the wake of political change (the collapse of the Soviet Union
in 1991). This dramatic change in land use is unique in its
kind in the whole 20th century, making the difference of the
Central Asian grassland to other regions more significant.
This historical development is also an excellent opportunity
to improve the understanding of the linkages between carbon
balance, climate, and land use change in dryland ecosystems
with the help of biophysical parameters which reveal knowl-
edge about the landscape’s dynamic.
Within this scope, the presented paper
(i) gives a brief review of the available LAI products for
Central Asia and especially Kazakhstan,
(ii) outlines their suitability for ecological modelling in
grasslands of Central Asia, and
(iii) gives an outlook with respect to future research
needs.
2. LAI Estimation from Remote Sensing and
Ground Truth Data
Between LAI and light interception, an inverse exponential
relation has been found, which is linearly proportional to the
primary production rate. Gross primary production (GPP)
is the rate at which producers inside an ecosystem capture
and store a given amount of chemical energy as biomass in a
given time span. Some fraction of this fixed energy is used
by primary producers for cellular respiration and main-
tenance of existing tissues (i.e., “growth respiration” and
“maintenance respiration,” as discussed by [6]). The remain-
ing fixed energy in form of plant mass (i.e., product of
photosynthesis) is referred to as net primary production
(NPP = GPP − respiration [by plants]).
NPP is the rate at which all the plants in an ecosystem
produce net useful chemical energy. It is equal to the differ-
ence between the rate at which the plants in an ecosystem
produce useful chemical energy (GPP) and the rate at which
they use some of that energy during respiration. Some NPP
goes toward growth and reproduction of primary producers,
while some NPP is consumed by herbivores. Both gross
and net primary production is measured in units of mass/
area/time. In terrestrial ecosystems, the mass of carbon per
unit area per year (g C/m2/yr) is most often used as the meas-
urement unit (e.g., for assessment of CO2 sequestration). LAI
measurements are a helpful tool to predict photosynthetic
primary production (e.g., GPP, NPP).
LAI estimation from remote sensing data is done by
the analysis of multispectral and multidirectional surface
reflectance signatures of vegetation that performs photosyn-
thesis [7]. In general two basic approaches are used to get LAI
from surface reflectance.
(1) Empirical or semiempirical relationships between
LAI and vegetation indices (i.e., NDVI, combination
of surface reflectances) are used [7]. The vegetation
index (VI) is designed to maximize sensitivity to the
vegetation characteristics while minimizing disturb-
ing factors such atmospheric noise, soil background,
or view-illumination geometry. These relationships
between LAI and VI’s are specifically calibrated for
distinct vegetation types using either coexistent in
situ LAI and reflectance measurements or simula-
tions from canopy radiation models [8, 9].
(2) Inversion of a radiative transfer model which models
surface reflectance from canopy structural character-
istics (including LAI), soil and leaf optical properties,
and view-illumination geometry is used [7, 8, 10].
lookup tables (LUT, [11]) and neural networks [10,
12] are the main inversion techniques deriving LAI
from radiative transfer models. The expected range
of the model parameters is either set up for each
vegetation type (e.g., MODIS algorithm) or globally
modelled (e.g., CYCLOPES algorithm).
Ancillary to the above-mentioned basic approaches, pre-
or postprocessing steps are used to remove residual atmo-
spheric, cloud contamination, and view-illumination effects
inside the satellite data. In many cases compositing tech-
niques are calculated to filter or smooth reflectance (e.g.,
CYCLOPES algorithm, MODIS algorithm, GLOBCARBON,
or CCRS algorithms) [7]. All techniques try to identify noisy
data in the LAI seasonal time series in order to replace them
by smoothed LAI values [13–16]. Tan et al. [16] for instance
built up phenology metrics based on temporally smoothed
and spatially gap-filled MODIS indices as LAI, Normalized
Difference Vegetation Index (NDVI), and Enhanced Vegeta-
tion Index (EVI) over North America. The spatial coverage
of this data set is more complete than other phenology pro-
ducts. This originated from the quality of the smoothed and
gap-filledMODIS data that was produced using an enhanced
version of the TIMESAT algorithm [15].
Together with other remote sensing products as land use/
land cover (LULC) maps, the LAI is used as input data for
NPP models or biomass models. Land cover is an input of
LAI algorithms, and land cover maps are widely used to
parameterize the biophysical properties of plant canopies
in models. Mostly, supervised classification algorithms are
used to generate land cover maps that characterize the vege-
tation types required for LAI and Fraction of Absorbed
Photosynthetically Active Radiation (fAPAR) retrievals from
remote sensing data such as MODIS. Inside this process,
the sensitivity of remote sensing-based retrievals of LAI and
FAPAR to land cover information is used to parameterize
vegetation canopy radiative transfer models. Figure 1 shows
the land-cover distribution over Kazakhstan and middle Asia
based on the MODIS landcover map.
Added to Figure 1 is Table 1 with concrete data about the
total numbers of pixels for each land-cover type.
This information in form of land use/land cover data and
LAI data allow the assessment of ground pixels in terms of net
primary productivity.
Journal of Sensors 3
0 1000 2000
N
E
Cropland
Grassland
Shrubland
Savanna
Forest
Wetland
Barren
Tundra
Irrigated cropland
Cropland mosaics
51◦45 57◦30 63◦15 69◦00 74◦45 80◦30 86◦15
40
◦ 1
5
46
◦ 0
0
51
◦ 4
5
(km)
Figure 1: Land-cover distribution over Kazakhstan andMiddle Asia
based on theMODIS land-cover map (source: Propastin et al. [17]).
Table 1: The total number of pixels within each land-cover type, its
area and percentage in Central Asia based on the 2001 MODIS land
cover map (source: Propastin et al. [17]).
Land cover type
Number of
pixels
Area in
mill·km2
Percentage,
%
Cropland 9008 0.609 10,92%
Irrigated cropland 4163 0.266 4,79
Cropland mosaic 18535 1.186 21,67
Grassland 26951 1.725 31,32
Shrubland 13186 0.844 15,2
Savanna 2077 0.133 2,3
Wetland 3135 0.201 3,61
Forest 6616 0.423 7,59
Tundra 332 0.021 0,38
BSV∗ 2771 0.177 3,13
Total 86774 5.578 100
BSV∗: barren or sparsely vegetated.
The assessment of the pixel-based primary productivity
can be used to improve estimates of the mentioned parame-
ters on a continental scale in order to increase the accuracy
of the flux predictions by providing timely judgements of the
NPP. The knowledge about how vegetation changes over time
and space is revealed by the knowledge of how reflected solar
radiation is modified by the vegetation. In order to derive this
knowledge, sensors that acquire information in the visible
(VIS) and near-infrared (NIR) parts of the spectrum are
analysed.
Most of the available remote sensing data sources during
the past years have been satellite sensors with a low number
of spectral bands in the visible and NIR wavelengths (e.g.,
AVHRR, Landsat). The needed plant parameters like LAI or
fraction of absorbed photosynthetic active radiation (fAPAR)
can only be roughly estimated. fAPAR is the fraction of
photosynthetic active radiation (400–700 nm) absorbed by
vegetation.While fAPAR is a key variable in the assessment of
vegetation productivity and yield estimates, the LAI delivers
valuable input more to climate and hydrologic modeling.
Whereas fAPAR shows a positive linear relationship with
increasing NDVI, LAI is nonlinearly related to NDVI, satu-
rating at LAIs of 3–6, depending on the vegetation type [18].
In order to estimate LAI and fAPAR from remotely sensed
data, canopy structural types must be defined as those which
exhibit different NDVI/LAI or fAPAR relations from one
another. Therefore, many classification schemes, which are
based on ecological, botanical, or functional metrics, are not
necessarily suitable for LAI and fAPAR retrieval. The utility
of this relationship depends on the sensitivity of these var-
iables to canopy characteristics [18]. The general relation-
ships between VI such as NDVI and fAPAR were found to be
linear. Nonlinear models gave a better fit for the VI/LAI rela-
tionship. In general the relation between fAPAR and NDVI
is stronger than between LAI and NDVI. In situ measured
fAPAR andNDVI normally show a strong linear relationship,
suggesting that covariance between fAPAR and NDVI is
insensitive to variations in leaf angle distribution (LAD) and
vegetative heterogeneity [18, 19]. A strong linear relation also
exists between MODIS fAPAR and NDVI but with weaker
regression coefficients than the in situ relationship because of
MODIS tendency to overestimate fAPAR (around 8 to 20%).
The fAPAR/NDVI relations are reported not to apply on a
global scale but are only valid for similar sun-sensor view
geometry and soil colour [18].
All algorithms to derive these estimations about LAI or
fAPAR are empirical and are based on a common relation-
ship between the recorded signal and the respective plant
parameter [19]. This oversimplification often leads to low
accuracies in estimating these parameters and at last of GPP
and NPP.
Estimations of fAPAR relying only on this type of simple
relationship cannot differentiate between the absorption by
photosynthetically active plant elements and the absorption
by other plant stuff and soil. This is the main problem of the
older sensors like AVHRR which on the other hand delivers
the longest time series of observations on earth. Sensors of
a newer generation like MODIS (launched in 1999) and
MERIS (launched in 2002) open up new vistas. These new
generation sensors and future advanced sensor developments
(e.g., modified MERIS on SENTINEL-3, EnMap, NPOES)
have more spectral channels at large and in the VIS and NIR
domain in particular. Besides better spatial resolution, a
better radiometric accuracy is available with these sensors.
With the use of the newer sensors and in due considera-
tion of different observation geometries during consecutive
overpasses, it is possible to implement different techniques
to invert models tracing the radiation transfer in vegetation.
These models can be divided into coupled leaf and canopy
radiation transfer models (e.g., PROSPECT and SAIL mod-
els, as discussed by Jacquemoud et al. [20]) or into a simple
canopy component as the model inversion scheme men-
tioned above [21]. The well-known PROSPECT leaf optical
properties model and SAIL canopy bidirectional reflectance
model, also called PROSAIL in combination, have been used
4 Journal of Sensors
1.5
1.2
0.9
0.6
0.3
0
1 41 81 121 161 201 241 281 321 361
(Day)
Steppe grassland
Short grassland
LA
I
(a)
(Month)
1.5
1.2
0.9
0.6
0.3
0
Steppe
Short grassland
Semidesert
1 2 3 4 5 6 7 8 9 10 11 12
LA
I
(b)
Figure 2: Typical time series of LAI over different grassland types of Kazakhstan derived from 8-day MODIS LAI product (a) and monthly
AVHRR LAI product (b).
for about eighteen years to study plant canopy spectral and
directional reflectance. PROSAIL has also been used to de-
velop new methods for retrieval of vegetation biophysical
properties. It combines the spectral variation of canopy
reflectance, which is mainly related to leaf biochemical con-
tents, with its directional variation, which is primarily related
to canopy architecture and soil/vegetation contrast. This
combination is important for the estimation of canopy bio-
physical/structural variables for applications in agriculture or
ecology, at different scales (for review see [22]).
In order to perform an operational inversion of huge
mounds of data, often lookup tables (LUTs) or neural net-
works techniques are applied. Plant parameters derived by
the inversion of radiation transfer models offer a better and
more precise characterisation of the vegetation properties.
During the last years, models have been designed which are
able to generate parameters such as leaf angle distribution
(LAD), leaf area index (LAI), chlorophyll content, or canopy
height [11, 11, 23, 24].
Handling this information inside radiation transfer
models, fAPAR can be calculated with higher accuracy than
with the simple functional relationship mentioned above.
The LAI is hence a very important factor for the quantitative
characterisation of canopies and the phenology of plants.
Phenology, which is the plants developing during the growth
period specific to the region where they grow, can be traced
by LAI. This seasonal developing of the plants can be quanti-
fied as a time series of LAI which is a very important appli-
cation to compare it with other environmental parameters
(e.g., precipitation, temperature, soil organic matter) for
ecological modelling.
In agriculture, phenology starts typically after sowing
with an LAI near 0 (bare soil). The LAI increases during the
growth period to a maximum value that is crop specific (e.g.,
5 for maize).
Plant production models exist in varying levels of scope
(physiological, individual plant, crop, geographical region,
global) and of generality. The model can be crop specific
or be more generally applicable. The crop growth model
SUCROS (open source) has been developed during more
than 20 years and is based on earlier crop models. The
IRRI (International Rice Research Institute) andWageningen
University more recently developed the rice growth model
ORYZA2000 (open source). The United States Department
of Agriculture (USDA) has sponsored a number of applicable
crop growth models for various major crops, such as cotton,
soy bean, wheat, and rice. Other widely-used models are
the precursor of SUCROS (SWATR), CERES, and several
incarnations of PLANTGRO, SUBSTOR, the FAO-sponsored
CROPWAT, AGWATER and the erosion-specific model
EPIC.
Today, the LAI is frequently used for ecological modelling
of biomass and the characterization of landscape dynamics.
Most models need the LAI as input parameter or as surrogate
to derive other plant parameters. For this reason long time
series of LAI (many years) are needed for change detection.
In Figure 2 typical time series of the LAI for a steppe area in
Kazakhstan are shown.
2.1. Typical Values of Ground-Based LAI. The LAI can be
explicitly measured in situ using a variety of techniques in-
cluding destructive sampling, allometry, and optical obser-
vation methods (hemispherical photography, see [1, 23]).
The disadvantages of these techniques are that they are both
geographically limited and time and costconsuming. Table 2
summarizes typical values of grassland LAI measured in
different world regions. A benchmark of typical values and
ranges of LAI for variety of biomes is given by Scurlock et al.
[25]. In general, the LAI values reported for Central Asia
(Kazakhstan) go well with the range of LAI values re-
ported by [25] for corresponding biomes (as discussed by
Propastin and Kappas [26]). They are also consistent with
ground-measured LAI in grassland/shrubland of the western
Sudano-Sahelian zone in Senegal [15], desert/shrubland in
Journal of Sensors 5
Table 2: Some of previous ground-based estimates of leaf area index in arid and semiarid zones.
Country/region Biome LAI Reference
USA/Arizona Desert/shrubland 0.93 ± 0.45 Whittaker and Niering [27]
USA/New Mexico
Grassland 0.75 ± 0.1 Asner et al. [29]
Shrubland 1.58 ± 0.3
Senegal/Sahel Grassland 0.15 – 2.5 Fensholt et al. [19]
Australia
Shrubland 0.36 ± 0.07 Hill et al. [30]
Grassland 0.50 ± 0.2
Woodland 1.89 ± 0.64
Kazakhstan
Grassland 0.22–0.90 Propastin and Kappas [26]
Shrubland 0.10–0.70
Worldwide
Grassland 0.29–5.47 Scurlock et al. [25]
Shrubland 0.4–4.5
Cropland 0.2–8.7
Arizona [27], and the northern Chihuahuan Desert in New
Mexico, USA [28]. The intercomparison with other studies
in grasslands gives us an indication for the possible range of
LAI values of the grassland biome in Kazakhstan.
2.2. Estimation from Remote Sensing Data. Remote sensing
provides the most feasible platform for spatially and tem-
porally continuous observations of biophysical parameters at
the scales from local to global [28, 31–34].
The impossibility to differentiate between the absorption
by photosynthetically active plant elements and the absorp-
tion by other plant stuff and soil is the main problem of the
older sensors like AVHRR. Sensors of a newer generation like
MODIS andMERIS open up new views on this problem [20]
or into a simple canopy component as the model inversion
scheme mentioned above [21]. The PROSAIL model has also
been used to develop new methods for retrieval of vegetation
biophysical properties. It combines the spectral variation of
canopy reflectance, which is mainly related to leaf biochemi-
cal contents, with its directional variation, which is primarily
related to canopy architecture and soil/vegetation contrast
[29, 35]. This combination is important for the estimation
of canopy biophysical/structural variables for applications in
agriculture or ecology, at different scales (for review see [22]).
The LAI is hence a very important factor for the quanti-
tative characterisation of canopies and the phenology of
plants. Phenology can be traced by LAI. This seasonal de-
veloping of the plants can be quantified as a time series of
LAI which is a very important application to compare it with
other environmental parameters (e.g., precipitation, tem-
perature, soil organic matter) for ecological modelling. In
grassland, phenology starts typically with a value of LAI near
0 (bare soil). The LAI increases during the growth period to a
maximum value that is cover specific (e.g., 1.0–1.2 for steppe
grassland). In Figure 2 typical time series of the LAI for
two major grassland types in Kazakhstan are shown, and in
Figure 3 typical time series of the LAI derived from MODIS,
Spot VGT, and AVHRR products for broadleaved forests over
Kazakhstan are compared. In Figure 4 a spatial comparison
(Month)
8
7
6
5
4
3
2
1
0
LA
I
MODIS
AVHRR
Spot VGT
Jan Mar May Jul Sep Nov
Broadleaved forest
Figure 3: Typical time series of LAI over broadleaf forests of
Kazakhstan derived from monthly MODIS, Spot VGT, and AVHRR
LAI product in 2002.
between Spot VGT andMODIS LAI to AVHRR LAI is shown,
where LAI values below 1.8 have a higher correspondence.
3. Available Satellite-Based LAI Data Sets for
Grassland of Kazakhstan
In the next chapters LAI data sets delivered by different
remote sensing sensors will be specified. Global LAI data sets
are generally produced using various approaches and algo-
rithms applied to many sensors data in the frame of spe-
cialized projects: CYCLOPES, GEOLAND, VGT4AFRICA,
POLDER, AMMA, and others. In general, for global and
regional LAI products, we find three main sensors as data
providers for LAI derivation over Kazakhstan: AVHRR,
SPOT VEGETATION, and MODIS. The described global
LAI products are available on an operational basis and are
restricted to a spatial resolution not higher than 1000m ×
1000m. The LAI products from higher resolution sensors
6 Journal of Sensors
Sp
ot
 V
G
T
/M
O
D
IS
 L
A
I
8
6
4
2
0
0 2 4 6 8
AVHRR LAI
Spot VGT
MODIS
Figure 4: Spatial comparison of Spot VGT/MODIS LAI to AVHRR
LAI cross over all land cover classes.
exist only on a local scale and only for short time periods
(sometimes only for one over flight).
3.1. Global LAI Products
3.1.1. LAI Derived from AVHRR. LAI time series from
AVHRR (Advanced Very High Resolution Radiometer) offer
the longest coherent time series on the world. Boston Uni-
versity derived a global LAI data set from AVHRR data. The
spatial resolution is available in three products: 8 km, 16 km,
and 0.5◦ resolution. The 16 km and 0.5◦ resolution LAI time
series are available as monthly composites from July 1981
until May 2001, while the new 8 km AVHRR LAI product
covers the period from July 1981 to September 2006. These
products were produced from an improved version of the
AVHRR Pathfinder Land NDVI Data set (source: Global
Monitoring and Modelling Group (GIMMS), see [36]) using
LAI versus NDVI relations build up by a radiative transfer
model fromMyneni [8, 37]. The LAI is produced using a land
covermapwith 9 classes: water, nonearth, nonvegetated land,
grass, shrub, broadleaf crops, savanna, broadleaf deciduous
trees and needle leaf tree. Validation of the AVHRR LAI
data set revealed good suitability of this product for climate
simulations (see [38]).
The LAI products are stored as flat binary files (unsigned
integer, 0–254). The 16 km LAI product is presented in
Goode’s projection with 2492 columns and 1084 rows. The
0.5◦ LAI product is presented in geographic projection (lat/
long projection) with 720 columns and 360 rows. A general
problem of this data set is that it is only available until 2006
and it works with coarse land use/cover classes. An example
of monthly AVHRR—LAI (June 2000) for Central Asia (Ka-
zakhstan) with 16 km spatial resolution is shown in Figure 5.
All global AVHRR LAI data sets can be downloaded at
http://cliveg.bu.edu/modismisr/index.html.
3.1.2. LAI Derived from the SPOT VEGETATION Instrument.
The LAI product derived from VEGETATION (medium
resolution sensor aboard SPOT4 and SPOT5 satellites) is
0 1 2 3 4 5 6
Figure 5: Mean peak season AVHRR-LAI (June 2000) over Kazakh-
stan.
based on a method, defined by the CSE team of INRA Avi-
gnon (http://w3.avignon.inra.fr/valeri/). In version 3 of the
CYCLOPES algorithms LAI, vegetation fractional cover
(Fcover) and fAPAR are assessed by inverting the radiative
transfer model SAIL using neural networks [39].
The LAI is calculated as function of vegetation fractional
cover, the leaf projection factor, and the clumping index,
whereas the second and third variables represent biome-
specific constants. This method is applied to VEGETATION
data. More details on the CYCLOPES algorithms can be
found in [40]. The LAI, Fcover, and fAPAR (1999–2003) are
packed in a product called “Biophysical”. They are provided
in tiles of 10◦ × 10◦ covering the whole globe in plate-carre´e
projection. The CYCLOPES LAI takes an active part inside
a validation process of existing global LAI products, coordi-
nated by SFC/NASA in collaboration with POSTEL (Poˆle de
Observation des Surfaces continentales par Tele´de´dection)
and other product providers (as discussed in Garrigues et al.
[7]).
The validation consists of
(i) the intercomparison with LAI products derived in the
frame of other projects (MODIS, GLOBCARBON,
ECOCLIMAP, CCRS) [7],
(ii) the comparison with insitu measurements collected
over the experimental site of ground networks and
up-scaling approaches using high resolution satel-
lite images (full list at http://lpvs.gsfc.nasa.gov/lai
intercomp.php).
First results, presented at the Global Land Monitor-
ing Workshop held in Missoula (2006), showed that the
CYCLOPES LAI in comparison displays the best spatio-
temporal consistency [7]. Further, it fits best the ground
measurements, in spite of a low LAI level over the dense
forests.
The CYCLOPES LAI products (hdf-format) can be
downloaded from http://postel.mediasfrance.org/sommaire
.php3?langue=English.
Further studies after the Missoula event 2006 concen-
trated on the development of LAI integration algorithms that
incorporate existing multiple LAI products [41]. Wang and
Liang [42] presented a new data integration method based
on empirical orthogonal function (EOF) analysis. The EOF
integration algorithm can be used on both fine and coarse
spatial resolution. Comparisons with high-spatial-resolution
LAI referencemaps from 12 sites over North America showed
that the proposedmethod can improve LAI product accuracy
Journal of Sensors 7
[42]. The improvement of this method was more significant
for MODIS as for CYCLOPES products.
3.1.3. LAI from the GEOLAND Project. GEOLAND was a
European FP6 Integrated Project aiming at providing to
GMES (Global Monitoring of Environment and Security,
EU) services in the “water and carbon,” “crop production
for food safety,” and “land cover change” areas the biogeo-
physical products that they need. POSTEL was leader of the
GEOLAND Biogeophysical Parameter Core Service (CSP)
and distributes to users several CSP products: leaf area
index, fraction of absorbed PAR, fraction of vegetation cover,
surface reflectances, burnt areas, surface albedo, downwelling
shortwave and longwave radiation fluxes, surface temper-
ature, precipitation, soil moisture, evapotranspiration, and
water bodies. The action is going on in the framework of FP7
Collaborative Project GEOLAND-2 which started in Septem-
ber 2008.
GEOLAND aims to build up a European capacity for
GMES. Two core services have been established to serve the
GEOLAND observatories, providing them with basic geo-
information products. The Core Service “biogeophysical”
Parameter (CSP) uses the CYCLOPES LAI derived from
VEGETATION data to elaborate customized product accord-
ing to theObservatory Natural Carbon (ONC) requirements.
ONC needs 0.5◦ × 0.5◦ tiled averages (mean and standard
deviation values) maps of LAI for 8 vegetation classes
(“conifer evergreen forest,” “deciduous forest,” “broadleaf
evergreen forest,” “grassland C3,” “grassland C4,” “crops C3,”
“crops C4”). LAI is also provided to the Observatory Food
Security and Crop Monitoring (OFM) for crop yield studies.
For ONC needs, the average and the standard deviation
values are calculated over boxes of 0.5◦ for 8 vegetation types.
As the CYCLOPES LAI is actually an effective LAI, a clump-
ing index during the customisation is applied. This cover-
dependent empirical clumping index is derived using the
approach suggested by [43]. The number of valid pixel in the
boxes of 0.5◦ for each class is also provided as a data layer.
The customized 10-day LAI products currently available
are derived from version 1 of CYCLOPES products, for
years 2002 and 2003. They are provided with an algorithmic
description and a readme file. The GEOLAND LAI products
for 2002 and 2003 (presented in raw binary format) can be
downloaded from http://postel.mediasfrance.org/sommaire
.php3?langue=English.
3.1.4. LAI from MODIS. The Boston University, Department
of Geography, delivers global LAI products based on Terra/
Aqua-MODIS Fraction of Absorbed Photosynthetic Active
Radiation (fPAR) products (MOD15A2). For a detailed
description of MOD15A2 product, see Knyazikhin et al. [11]
andMyneni et al. [8, 37]. The latestMOD15A2 product (Col-
lection 5) replaced the old 6-biome LAI/FPAR biome map
with new 8-biome map. Broadleaf and needle leaf forests
classes were splitted into deciduous and evergreen subclasses.
Algorithm refinements were carried out to improve quality
of LAI/FPAR retrievals, and consistency with field measure-
ments over all biomes, but with major focus on woody vege-
tation.
Each biome represents a pattern of the shape of an
individual tree (leaf normal orientation, stem-trunk-branch
area fractions, leaf and crown size) and the entire canopy
(trunk distribution, topography), as well as patterns of spec-
tral reflectance and transmittance of vegetation elements.
Moreover, the soil type and understorey type are considered
as biome characteristics, which can alter continuously within
given biome specific thresholds. The distribution of leaves
is described by the leaf area density distribution function
which also depends on some continuous parameters. The
eight biomes are grasses and cereal crops (biome 1), shrub
(biome 2), broad leaf crops (biome 3), savanna (biome 4),
evergreen broad leaf forest (biome 5), deciduous broad leaf
forest (biome 6), evergreen needleleaf forest (biome 7), and
deciduous needleleaf forest (biome 8). The following land
cover classes are excluded from the calculation: urban, built-
up class, permanent wetlands, marshes, perennial snow, ice,
tundra, barren, desert, or very sparsely vegetated areas, water
(ocean or in land water).
The MODIS LAI products are available at https://lpdaac
.usgs.gov/get data/data pool.
The global LAI products are available as monthly com-
posites with different spatial resolution (1 km, 4 km, and
0.25◦) for the period from February 2000 till present. Global
8-days composites are available with a spatial resolution of
0.25◦ starting at Julian day 57 of the year 2000.
3.1.5. Suitability of the Available Global LAI Products for
Applications in Central Asia. A short summary of the avail-
able global LAI products is listed in Table 3. For global appli-
cations various consistent time series of LAI exist with
1 km × 1 km spatial resolution. Independent assessment of
product quality is a critical step to the success of a global LAI
product usage. The satellite-modelled LAI products evidently
simplify reality and require extensive validation, which is
based on ground truth information from a possible greatest
number of sites representing all the biomes included in the
model. For instance, the reports about activities of MODIS
LAI validation in different earth regions and various biomes
climb in recent literature (e.g., [7, 19, 30, 44]). Results from
validation studies around the world are being successfully
used to adjust and refine MODIS LAI [45]. However, all
the validation activities have been mostly focused on certain
biomes (predominantly forest and cropland) in test sites
located in the western hemisphere, while validation studies
from the eastern hemisphere have been very scarce. Similar
problem also concerns other global LAI products. With
respect to the grassland biome, MODIS LAI products have
been checked against ground-based data only in a semiarid
region of Africa [19, 30, 46].
The Eurasian temperate grassland, representing the
world largest grassland biome, with its east-west extension
of more than 6000 km and a north-south extension of about
1490 km, has been out of scope of extensive validation efforts.
The existing global LAI products have not been checked in
any region or biome of Central Asia yet. In our current
knowledge, there are no published studies on validation of
any global LAI products in Central Asia. Within this scope,
an ongoing research work on validation of the existing global
8 Journal of Sensors
Table 3: Available LAI products with global coverage.
Sensor Coverage Project/product Spatial resolution Temporal resolution Temporal availability
AVHRR
Global ISLSCP 1◦ (×km) Monthly 1987-1988NOAA-9
and NOAA-11
AVHRR Global Boston University 0.5◦ (×km) Monthly 07/1981 to 05/2001
AVHRR Global Boston University 16 km Monthly 07/1981 to 05/2001
AVHRR
Global Ecoclimap 1 km Monthly
1992 to 1993
1997
VEGETATION Global Cyclopes 1 km 10 days 1999 to 2003
VEGETATION Global Geoland 0.5◦ 10 days 2002 to 2003
VEGETATION
Global Globcarbon 1 km/10 km/0.25◦/0.5◦ 10 days 1998 to 2007
ATSR
POLDER Global Polder-1 6 km 10 days 1996 to 1997
POLDER Global Polder-2 6 km 10 days 2003
MODIS
Global MOD15A2 1 km Daily and 8 days 2000 until present
TERRA/AQUA
MODIS
Global MOD15A3 1 km 4 days July 2002 until present
TERRA/AQUA combined
NASA: national aeronautics and space administration.
AVHRR: advanced very high resolution radiometer.
ISLSCP: international land surface climatology project.
MODIS: moderate resolution imaging spectroradiometer.
POLDER: polarization and directionality of the earth’s reflectance.
VEGETATION: vegetation sensor aboard spot4 and spot5 satellites.
LAI products in a grassland region in Central Kazakhstan
is of great importance. This work is being carried out at
the Department of GIS and Remote Sensing (University of
Go¨ttingen, Germany) and uses extensive field measurements
of LAI as reference data for validation of global LAI products.
As first, the validation of MODIS LAI product has been
finished by this research group and its results will be
published. Results of the MODIS LAI validation showed that
MOD15A2 overestimates the ground-based LAI by 10–15%
during the growing season. This study also found great prob-
lems with MODIS LAI outside the growing season: LAI
values of 0.1–0.15 weremaintained throughout the year, even
during periods with air temperature below 0◦C. Similar
results have been reported by [19] for a semidesert grassland
in Africa.
Another strategy for validation of global LAI products is
a comparison of different LAI products with each other. LAI
products intercomparison enables to separate differences in
LAI values caused by the satellite input data from differences
caused by the used LAI algorithm [47, 48]. The intercom-
parison and validation of the existing global LAI products,
coordinated by GSFC/NASA in collaboration with Postel and
other product providers, revealed that, for the time period
1999 to 2003, it is recommended to use the CYCLOPES-
VEGETATION data (whole study, described in [7]). When
LAI time-series with 1 km × 1 km spatial resolution from
2000 to present are needed, the only source is MODIS with
the MOD15A2 product. The time series of VEGETATION
and MODIS differ in the temporal resolution (VEGETA-
TION: 10 days; MODIS: 8 days). Therefore, a harmonization
of both time series is needed.
With respect to the spatial dimension of the Central
Asian grassland and its importance for global carbon cycle
and climate change, the work on validation of the existing
global LAI products should be strengthened.
4. Available Local and Regional LAI
Data Sets for Central Asia
For local or regional applications with spatial resolutions of
less than 1000m× 1000m, no global LAI dataset is available.
All higher spatial resolution sensors with a swath width of a
few kilometres to 200 km (Landsat ETM+) have a revisit time
of 2 weeks or more. In order to produce LAI time series from
these sensors, temporal interpolation is required. Therefore,
the number of observations for one test site is limited by the
number of cloud free over flights.
The use of biome-specific parameter values in algorithms
of the global LAI products neglects the variability of the
landscape within an individual biome. At the local to regional
scale, the algorithm can be improved by incorporating per-
pixel information on the above-mentioned parameters into
the modeling approach as shown in recent studies (e.g., [49,
50]). Unfortunately, there are only a few published studies
from Central Asia or its regions presenting satellite-based
LAI products produced at local and regional scale.
In the study by Propastin and Kappas [51], fine-reso-
lution Landsat ETM+ imagery was combined with in situ
measured data tomap LAI over a 200–300 km large grassland
region in Central Kazakhstan. The authors tested usability of
different vegetation indices and transfer techniques (statisti-
cal regression, geostatistics) for predicting LAI from Landsat
Journal of Sensors 9
ETM+ data. They also examined consistency between con-
tact and noncontact LAI measurement methods in field and
found good correspondence between LAI values calculated
from grass harvesting technique (contact method) with LAI
values produced from hemispherical photography (non-
contact method).
Propastin and Kappas [26] developed a 1 km spatial
resolution LAI data set for the semidesert and steppe biomes
in Kazakhstan using SPOT-VGT-based NDVI and in situ
measurements. Their empirical approach consisted of two
steps. In the first step, they used a simple statistical regression
between field-measured LAI values and a fine-resolution
satellite data (Landsat ETM+) for mapping LAI over the area
of the field measurements. In the second step, a regression
model between the Landsat-based LAI map, which had
previously been degraded to the spatial resolution of 1 km,
and SPOT-VGT NDVI was produced at the per-pixel basis.
After that, LAI was mapped over the whole area of semidesert
and steppe vegetation in Kazakhstan. The produced LAI data
set was then employed in modelling net ecosystem exchange
in Central Kazakhstan (see Propastin and Kappas [52]).
5. Designing Improved LAI Data Sets for
Central Asia (Kazakhstan)
Apart from the local and regional LAI data sets described in
Section 4, a work on a development and validation of new
coarse-resolution LAI products for the grassland of Central
Asia and Kazakhstan is underway. The first product is based
on the 8 km spatial resolution AVHRR data (Goettingen GIS
and Remote Sensing GGRS-data set, see Propastin et al.
[53]) while the second product employes the 1 km spatial
resolution SPOT-VEGETATION data (Propastin and Kappas
[26]). Both products use a satellite-based LAI algorithm
developed in the Department for GIS and RS at Georg-
August University of Go¨ttingen, Germany [50, 54]. This algo-
rithm uses a three-dimensional physical radiative transfer
model which establishes relationship between LAI, vegeta-
tion fractional cover and given patterns of surface reflectance,
view-illumination conditions and optical properties of veg-
etation. The model incorporates a number of site/region-
specific parameters, including the vegetation architecture
variables such as leaf angle distribution, clumping index, and
light extinction coefficient. For the application of the model
to Kazakhstan, the vegetation architecture variables were
computed at the local (pixel) level based on extensive field
surveys of the biophysical properties of vegetation in repre-
sentative grassland areas of Kazakhstan. Influence of view-
illumination conditions on optical properties of vegetation
was simulated by a view angle geometry model incorporating
the solar zenith angle and the sensor viewing angle.
After finishing the ongoing work, the new LAI products,
covering spatially the whole area of grassland in Central
Asia (Kazakhstan) and temporally the period 1982–2010 (for
the AVHRR-based product) and 1998–2010 (for the SPOT-
VEGETATION-based product), respectively, will be freely
available to the public use on the Google Earth Engine Ini-
tiative.
6. Conclusions
Remote sensing techniques have many advantages in LAI
estimation over traditional field measurement methods and
provide the potential to estimate LAI at different scales. This
paper reviewed LAI products available for environmental
and ecological modelling in the formerly Soviet Central Asia
(especially Kazakhstan) with a focus on grassland biome
and outlined issues of their application. The following main
conclusions can be made from the review.
(1) The studied region is characterized by its enormous
role in the global carbon cycle and global change.
However, ecological modelling studies in the for-
merly Soviet Central Asia cannot be supplied by
region-specific LAI data sets adequately.
(2) The use of existing global LAI products is limited
by their spatial resolution (1000m and coarser) and
unknown suitability for this region. No one of the
global LAI products has been compared with ground-
based data from the region. Validation of global LAI
products just begins in grassland of Kazakhstan. First
results of this validation work revealedmoderate con-
sistency of MODIS LAI product with ground-based
measurements at the peak of the growing season and
high inconsistency outside the growing season.
(3) There are only a few region-specific existing LAI pro-
ducts, which base on ground LAI from this region.
These products have successfully been used for eco-
logical modelling in the grasslands of Central Kaza-
khstan.
With these conclusions in background, future research may
focus on the amplification of validation of the existing global
LAI products, and the development of new region-specific
LAI products. By developing new LAI products for this
region, new generation sensors and future advanced sensor
developments that have more spectral channels in the VIS
and NIR domain may be used. With the use of the newer
sensors and in due consideration of different observation
geometries during consecutive overpasses it is possible to
implement different techniques to invert models tracing the
radiation transfer in vegetation.
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