Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment...

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Remote Sensing of Environment 91 (2004) 490–507

Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and

NDVI in a semi-arid environment using in situ measurements

Rasmus Fensholt*, Inge Sandholt, Michael Schultz Rasmussen

Institute of Geography, University of Copenhagen, Øster Voldgade 10, DK-1350 Copenhagen, Denmark

Received 4 August 2003; received in revised form 16 April 2004; accepted 18 April 2004

Abstract

On global and regional scales, earth observation (EO)-based estimates of leaf area index (LAI) provide valuable input to climate and

hydrologic modelling, while fraction of absorbed photosynthetically active radiation (fAPAR) is a key variable in the assessment of

vegetation productivity and yield estimates. Validation of moderate resolution imaging spectroradiometer (MODIS) LAI and fAPAR products

is an important prerequisite to using these variables for global modelling or for local water resource modelling and net primary production

(NPP) assessment, as in semi-arid West Africa and Senegal. In situ measurements of LAI and fAPAR from three sites in semi-arid Senegal

were carried out in 2001 and 2002 for comparison with remotely sensed MODIS data. The seasonal dynamics of both in situ LAI and fAPAR

were captured well by MODIS LAI and fAPAR. MODIS LAI is overestimated by approximately 2–15% and the overall level of fAPAR is

overestimated by 8–20%. Both MODIS LAI and fAPAR are characterised by a moderate offset, which is slightly higher than can be

explained by model and input data uncertainty. In situ fAPAR and normalised differential vegetation index (NDVI) for three different

vegetation types showed a strong linear relationship, suggesting that covariance between fAPAR and NDVI is insensitive to variations in leaf

angle distribution (LAD) and vegetative heterogeneity. A strong linear relation also exists between MODIS fAPAR and NDVI but with

different regression coefficients than the in situ relation because of MODIS’ tendency to overestimate fAPAR. The fAPAR/NDVI relations

found here, however, do not apply on a global scale but are only valid for similar sun-sensor view geometry and soil colour.

D 2004 Elsevier Inc. All rights reserved.

Keywords: fAPAR; In situ measurements; LAI; MODIS; NDVI; Sahel; Semi-arid

1. Introduction

Leaf area index (LAI) and fraction of photosynthetically

active radiation absorbed by vegetation (fAPAR) represent

two biophysically complementary ways of describing the

earth’s vegetated surfaces. LAI is generally defined as the

one-sided green leaf area per unit ground area in broadleaf

canopies (Myneni et al., 1997) and LAI gives an estimate of

the green leaf area of terrestrial vegetation. fAPAR is a

measure of how large a fraction of the sunlight leaves

absorb in the 0.4–0.7 Am spectrum and fAPAR thus

expresses a canopy’s energy absorption capacity. Moderate

resolution imaging spectroradiometer (MODIS) global cov-

erage satellite products of LAI and fAPAR (Collection 4)

have been released to the public recently. On a global scale,

both LAI and fAPAR are key variables in many climatic

0034-4257/$ - see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.rse.2004.04.009

* Corresponding author. Tel.: +45-35322526.

E-mail address: [email protected] (R. Fensholt).

models (Sellers et al., 1996, 1997) and models of net

primary production (NPP) (Running et al., 1999). In the

context of resource management goals laid out by the Kyoto

protocol (United Nations, 1992), earth observation (EO) of

these variables is the most effective means of collecting data

on a regular basis. On a regional scale, EO estimates of LAI

can provide valuable information for hydrological model-

ling (Andersen et al., 2002; Kite & Pietrorino, 1996) and

fAPAR is a key variable in the assessment of vegetation

productivity (Prince, 1991a,b).

When estimating the productivity of terrestrial ecosys-

tems from satellite images, canopy spectral measures of

vegetation intensity in relative units must be converted into

quantitative biophysical variables. This conversion has been

performed using models of varying complexity.

Top-down statistical models provide an empirical rela-

tionship between variables. A number of statistical studies

(Rasmussen, 1992; Tucker et al., 1983, 1985) have ana-

lysed the direct relation between the integrated normalised

differential vegetation index (NDVI) from the NOAA

R. Fensholt et al. / Remote Sensing of Environment 91 (2004) 490–507 491

AVHRR and yield or NPP. This approach is only applica-

ble on local to regional scale because it is based on the

assumption that incoming radiation and its conversion to

dry matter are constant in time and space, which is not the

case on a larger scale.

The intermediate level of model complexity is charac-

terised by parametric models, which represent complex

photosynthetic processes using a few biophysical variables.

The ongoing research has sought to establish more phys-

ically based relationships between vegetation development

and NDVI through the use of parametric models. Theo-

retically, most of these models originate from the light use

efficiency (LUE) concept proposed by Monteith (1972)

and seek to describe light absorption by plant canopies

through a relation between NDVI and fAPAR (Begue,

1993; Goward & Huemmrich, 1992; Hanan et al., 1995;

Le Roux et al., 1997). fAPAR is still expressed as a ratio

but can be converted into absorbed photosynthetically

active radiation (APAR) usually expressed in MJ/m2 by

multiplying the fAPAR ratio by incoming PAR. Using the

LUE concept for modelling of NPP is contingent on a

robust relationship between the biophysical variables

fAPAR and NDVI. Numerous authors have studied this

relation and there is general agreement that a stronger

relation exists between fAPAR and NDVI than between

LAI and NDVI (Myneni et al., 1995, 2002). Based on

satellite data, the relationship between NDVI and fAPAR

has been found to be linear or approximately linear for

green vegetation (Prince & Goward, 1995; Ruimy et al.,

1994) and by radiative transfer models (Begue, 1993;

Carlson & Ripley, 1997; Goward & Huemmrich, 1992;

Myneni & Williams, 1994). Gobron et al. (2000) propose a

new mathematical approach to derive vegetation index

formulae optimized to estimate the same vegetation prop-

erty fraction of fAPAR to assure as much as possible a

one-to-one relationship between VI and fAPAR. This

approach is used in the design of the MERIS vegetation

index (MGVI). Few studies have, however, tested the

relation against in situ measurements. Hatfield et al.

(1984) find a linear relation for wheat and Begue (1991)

finds a linear relation for millet. Lind and Fensholt (1999)

likewise demonstrate a linear relation between fAPAR and

NDVI for millet, grass savannah and sorghum, but Le

Roux et al. (1997) find the relation to be nonlinear for

humid savannah grassland. A number of factors are found

to influence the parameters of this linear relation. These

can be divided into external factors, including atmospheric

influence and view angle geometry, and canopy-related

factors, including leaf angle distribution (LAD), canopy

heterogeneity, soil–canopy reflectance interactions and

senescent material in the canopy. Based on a radiation

transfer model, Begue (1993) found the relation between

fAPAR and NDVI to be sensitive to varying solar zenith

angles, Van Leeuwen and Huete (1996) found that the

presence of dead leaves may alter the relation and Huete et

al. (1999) concluded that the relation between fAPAR and

NDVI is site-specific and often only valid when calibrated

for a given soil type.

Bottom-up models include highly complex ecosystem

simulation models describing the detailed interaction among

incoming radiation, individual leaves and the canopy as a

whole. These models often require a number of input

variables, which can be difficult to derive from EO data.

In practice, many of these variables are simulated in

additional models and stored in lookup tables. Coupling

EO data with in-depth ecosystem understanding thereby

enables spatially distributed estimation of vegetation

productivity.

MODIS LAI and fAPAR products are derived from a

global-scale process model. Retrieval of the variables is

based on biome-specific algorithms assigned specific a

priori assumptions about LAD and constants on, for exam-

ple, leaf, wood, litter and soil optical properties (Knyazikhin

et al., 1998a,b). This approach minimises the number of

unknowns when solving the inverse problem of retrieving

LAI and fAPAR from the atmospherically corrected and

bidirectional reflectance distribution function (BRDF) cor-

rected MODIS channel 1–7 (Myneni et al., 2000). The

algorithms compare observed surface reflectance to mod-

elled values for a broad range of canopy structures and soil

patterns representing natural conditions all stored in a

lookup table. Solving the inverse problem results either in

no solutions, a unique solution or multiple solutions for a

given set of reflectance properties. A given solution is

accepted if the uncertainty between observed and modelled

reflectance is less than the uncertainty on the observed

reflectance (Privette et al., 2002). The mean values of LAI

and fAPAR averaged over all acceptable solutions will

thereby be the final output. If no solution exists, a backup

routine using biome-specific conversion algorithms of the

vegetation index is applied for LAI and fAPAR assessment

(Myneni et al., 1997). For a detailed description of MODIS

fAPAR and LAI modelling, see Knyazikhin et al. (1999)

and Myneni et al. (2002).

Modelled LAI and fAPAR evidently simplifies reality,

and the validity of the modelling shortcuts must be tested

for a number of sites representing all the biomes included

in the model. Validation is an important step before using

models for global forecasting as evidenced by the recent

experiences of the Intergovernmental Panel on Climate

Change (IPCC) recently. Using the products for water

resource modelling and NPP assessment in a regional or

national context (like in the semi-arid West Africa and

Senegal), it is, however, equally important that the prod-

ucts are validated. The current study sites form part of the

global ‘‘fluxnet’’ subsites, currently the only sites covering

the grass savannah in West African Sahel. The aim of this

paper is twofold: to provide validation of the MODIS LAI

and fAPAR products for a semi-arid grass savannah in

Senegal, using in situ measured time series of these

variables, and to analyse the possibilities and constraints

of establishing a robust relationship between NDVI and

Fig. 1. The extent of the Sahelian region with Senegal located on the

Atlantic seaboard. The northern boundary of Sahel is often defined by the

100-mm isohyet, while the southern boundary is designated by the 600–

800 mm isohyets (Prince et al., 1995).

R. Fensholt et al. / Remote Sensing of Environment 91 (2004) 490–507492

fAPAR for both savannah and different crop types. This

will be done to answer the questions whether the quality of

the MODIS LAI and fAPAR products is good enough in a

semi-arid environment and how to assess fAPAR with a

maximum precision with the knowledge of actual vegeta-

tion conditions from in situ measurements. The last ques-

tion includes an evaluation of in situ measurements of

NDVI and fAPAR for millet, groundnut and savannah,

which are characterised by three different LADs to test the

relationship’s dependency on LAD. The in situ relation

between NDVI and fAPAR is compared to the

corresponding relationship derived from MODIS satellite

Fig. 2. MODIS 12 Land use classification (Land cover type 3, LAI/fAPAR biomes

data and is also evaluated against other data sources

reported in the literature.

2. Study area

Field measurements were carried out in Senegal, in the

western Sudano–Sahelian zone (Fig. 1). Senegal is charac-

terised by a pronounced rainfall gradient with annual totals

declining from south to north. Water availability is generally

considered the most important determinant for vegetation

growth in the drier parts of the Sudano–Sahelian zone

(Hendricksen & Durkin, 1986; Hielkema et al., 1986; Malo

& Nicholson, 1990), and the strong rainfall gradient is

clearly reflected in a corresponding gradient in the density

(Fig. 2) and type of vegetation.

Fieldwork was carried out in the semi-arid centre and

north of the country where rainfall averages 200–500 mm/

year. The rainy season lasts from July to September and

precipitation is sparse and fluctuating. Because of interan-

nual and intra-annual variations in rainfall, the vegetation is

often exposed to water shortage during the growing season,

periodically leading to severe droughts, food shortage and

production deficiencies. The land cover is predominantly

dry grasslands with scattered trees and bushes. The vegeta-

tion of the area consists of fine-leaved annual grasses such

as Schoenefeldia gracilis, Dactyloctenium aegupticum, Aris-

tida mutabilis and Cenchrus bifloures. Annual grasses with

a maximum height of 60 cm are often abundant, inter-

spersed with perennial grasses with a maximum height of 80

). Locations of the fieldwork sites are indicated. Pixel resolution is 1000 m.


cm (Ridder et al., 1983). Tree and shrub canopy cover

generally does not exceed 5% in the fieldwork area and is

mainly characterised by two species, Balinites aegyptiaca

and Boscia senegalensis (Diallo et al., 1991). In the vicinity

of villages, small fields of millet (Pennisetum glaucum L.)

and groundnut (Arachis hypogaea L.) are the most common

types of cultivation. The soil in the study area is charac-

terised as a poorly developed soil formed on sandy parent

material of dunes or fluvial deposits (less than 3% clay).

Soils have a reddish colour and have previously been

classified as arenosols (Batjes, 2001; FAO, 1995).

3. Data

The study is primarily based on the MODIS TERRA

Land satellite Collection 4 data and fieldwork measurements

from 2001 to 2002 for validation of the satellite-derived

variables. Landsat ETM+ data were used for comparing the

representativeness of the sites of in situ measurements to

MODIS 1-km LAI/fAPAR pixel size.

3.1. Fieldwork

Fieldwork was carried out at one location (Dahra) in

2001 and at three different locations in 2002 (the Dahra

site and two sites near Tessekre 10 km apart). Locations

are indicated in Fig. 2. All three sites are covered by grass

savannah. In situ measurements of radiation were carried

out from June to November in both years to calculate time

series of NDVI and fAPAR. Incident and reflected radia-

tion were measured in the PAR spectrum (400–700 nm)

and in the red and near-infrared wavelengths (bandwidth

40 nm and centred at 650/860 nm) to simulate the wave-

Fig. 3. Photograph of the Dahra site 1/9 2001. The picture illustrates the fieldwork

where height of the grass is approximately 50 cm (UTM: 452412; 1699018, zone 2

lengths used for NDVI monitoring by the Terra MODIS

sensor. Rainfall and other standard agro-climatic variables

like soil moisture and air and surface temperature were

additionally measured. The year 2002 was an extremely

dry year with rainfall below 50% of normal, shortening the

growing season and stunting vegetative growth. In 2001,

the rainy season was slightly drier than average. Fig. 3

illustrates the Dahra North site 2001 at the peak of the

growing season.

The radiometers used are produced by SKYE Instru-

ments (http://www.skyeinstruments.com). The instrumenta-

tion consisted of SKYE PAR Quantum sensors (SKP 215)

and SKYE PAR/NIR Quantum sensors (SKR 110) mounted

on a 6-m aluminium mast and data were collected using

Campbell CR10 data-loggers measuring every 10 min. LAI

was measured using a LAI-2000 Plant Canopy Analyser

(LI-COR, Lincoln, USA). Using a mobile aluminium mast

carrying the same equipment setup as at the permanent sites,

further measurements were conducted over various millet

and groundnut fields at varying phenological stages during

both years of fieldwork.

3.2. Satellite data

The specifications of the MODIS and Landsat satellite

products are given in Table 1.

MODIS Collection 4 data are a new and improved

version of MODIS Land science products. The most recent

LAI/fAPAR data products have well-defined uncertainties

and are currently in the validation phase. The MODIS 1000-

m LAI/fAPAR and VI products are based on the level 2

daily surface reflectance product (MOD09 series), which is

corrected for the effects of atmospheric gases, thin cirrus

clouds and aerosols. The correction scheme uses the

equipment mounted on a grass savannah in the peak of the growing season

8). The data logger is stored in the blue metal box in the image foreground.

http:\\www.skyeinstruments.com

Table 1

The MODIS and Landsat data products

Data product Year

MODIS/Terra Surface Reflectance Daily Level 2 Global

500 m ISIN Grid Collection 4 data (MOD09)

2001–2002

MODIS/Terra Leaf Area Index/fAPAR 8-day Level 4

Global 1-km ISIN Grid Collection 4 data (MOD15)

2001–2002

LANDSAT 7 ETM+ Level 1G product 2002 (4)


MOD05 product for water vapour, the MOD04 product for

aerosols, the MOD07 product for ozone and MODIS band

26 for detection of thin cirrus clouds (Vermote & Vermeu-

len, 1999). The MOD09 product comprises all MODIS

bands 1–7 and provides an estimate of the surface spectral

reflectance for each band, as it would have been measured at

ground level in the absence of atmospheric scattering or

absorption (Vermote & Vermeulen, 1999). As further input

for the calculation of the MODIS LAI and fAPAR, the

Collection 3 MODIS land cover product (MOD12Q1)

(Friedl et al., 2002) is used. Each of the six biome classes

is assigned surface characteristics regarding canopy archi-

tecture and optical properties of the vegetation derived from

a 3D radiative transfer model (Myneni et al., 1990). The

availability of satellite data and in situ measurements are

summarized in Table 2.

The overall accuracy of the MODIS LAI and fAPAR

products depends on both the model uncertainties and the

uncertainties of the input data. Model uncertainties inevi-

table emerge when forcing the natural range of variations in

optical properties into six biome classes. This type of

uncertainty generally depends on the amount of information

available (number and combination of spectral bands) when

retrieving biophysical parameters from surface reflectances

(Wang et al., 2001) and also on the temporal and spatial

Table 2

The temporal availability of MODIS, Landsat ETM data products and in situ me

Eight-day Period Year 2001

comp.

periodLandsat

7 ETM+

MODIS VI and

LAI/fAPAR

In

m

23 26/6–03/7

24 04/7–11/7

25 12/7–19/7

26 20/7–27/7

27 28/7–04/8

28 05/8–12/8

29 13/8–20/8

30 21/8–28/8 Yes Ye

31 29/8–05/9 30/8 Yes Ye

32 06/9–13/9 Yes Ye

33 14/9–21/9 Yes Ye

34 22/9–29/9 Yes Ye

35 30/9–07/10 Yes Ye

36 08/10–15/10 Yes Ye

37 16/10–23/10 Yes Ye

38 24/10–31/10 Yes Ye

39 01/11–08/11 Yes Ye

40 09/11–16/11 Yes Ye

resolution of the input data (Myneni et al., 2002). For dense

canopies, the reflectance from the lower leaf layers can be

obscured by the upper layers and the reflectance becomes

insensitive to model vegetation parameters. This problem of

saturation causes the dispersion of fAPAR/LAI solution

distribution to be large, affecting the reliability of the

retrieved output (Knyazikhin et al., 1998a; Myneni et al.,

2002).

Uncertainties in the MODIS surface reflectance input

channels are reported prior to the launch of the Terra

platform by Vermote et al. (1997) and Vermote (2000).

Even though input channels are atmospherically corrected,

reflectance variations of a vegetated surface are present,

originating from variations in sun-sensor geometry, cano-

py structure and uncertainties due to corrections for

atmospheric effects. The number of band used for the

LAI/fAPAR algorithm is currently restricted to the first

two bands (red and near-infrared) which is closely related

to the uncertainties of the different MODIS input bands

(Huang et al., submitted for publication). Atmospheric

effect at blue is much stronger than at red, and therefore

uncertainties in the atmospheric corrected surface reflec-

tance are larger at blue than at red (Wang et al., 2001).

Including more spectral band in the LAI/fAPAR algorithm

thus increases the information content, but due to the

accuracy in the input spectral bands, the overall quality of

the retrievals is reduced. Vermote (2000) estimates the

uncertainty in the MODIS red to 10–33% and 3–6% at

near-infrared, whereas the overall relative uncertainty for

red, near-infrared, green and blue is set to 16.8%. For

data classified as highest quality, Huang et al. (submitted

for publication) estimate average uncertainties in the

MODIS red and near-infrared surface reflectance to be

about 10–15%. The uncertainties on the red and near-

asurements

Year 2002

situ

easurements

Landsat

7 ETM+

MODIS VI and

LAI/fAPAR

In situ

measurements

30/6 Yes Yes

Yes Yes

Yes Yes

Yes Yes

1/8 Yes Yes

Yes Yes

17/8 Yes Yes

s Yes Yes

s Yes Yes

s Yes Yes

s Yes Yes

s Yes Yes

s 4/10 Yes Yes

s Yes Yes

s Yes Yes

s Yes Yes

s Yes Yes

s Yes Yes


infrared surface reflectance influence the LAI and fAPAR

output, and Wang et al. (2001) have estimated the overall

LAI and fAPAR dispersion for two biome types and

different configurations of spectral bands and relative

uncertainties. The LAI dispersion is increasing as a

function of vegetation intensity, but for values from 0

to 2, the LAI dispersion for red and near-infrared band

independent uncertainty is fairly constant at approximately

0.10–0.15. The derived fAPAR dispersions are not in-

creasing as a function of vegetation density and reach a

maximum level of dispersion of 0.05–0.1 for fAPAR

values around 0.1–0.2.

3.3. MODIS post-processing

The following processing procedures are not part of the

standard MODIS products but are performed by the authors.

MODIS Land data are originally projected onto an Integer

Sinusoidal (ISIN) mapping grid. The Land data have been

rectified to UTM coordinates using MODIS reprojection

tool available at http://edcdaac.usgs.gov/landdaac/tools/

modis/index.asp, and data are resampled using the near-

est-neighbour algorithm. A 16-day standard maximum

value composite (MVC) product was produced from the

daily MOD09 data and chosen over the MODIS 16-day

constrained view angle product (CV-MVC) as the standard

MVC was found to match in situ measured NDVI better

than MODIS CV-MVC (Fensholt & Sandholt, submitted for

publication). The 500-m NDVI products were resampled to

1 km again using the nearest-neighbour algorithm to facil-

itate comparison with MODIS fAPAR 1-km products.

When analysing the relation between the 8-day fAPAR

and 16-day MVC NDVI products, a simple maximum value

composite (MVC) of two 8-day fAPAR periods is used.

3.4. Landsat processing

Landsat scenes were geometrically rectified to 15-m

precision using the band 8 (15-m resolution) and ground

control points (GCPs) from the area. The Landsat acquisi-

tion dates listed in Table 2 are the cloud-free scenes

available for 2001–2002. It will not be the subject of this

paper to carry out direct comparisons of Landsat biophysical

variables with the MODIS products. The Landsat NDVI

values will merely be used as a tool for scaling in situ

biophysical measurements to the MODIS biophysical prod-

ucts, thereby facilitating comparisons between ground truth

level and satellite level.

4. Method

4.1. In situ measurements of LAI

Leaf area index (LAI) was measured approximately

every fifth day using the LAI-2000 Plant Canopy Analy-

ser, which calculates LAI from nondestructive radiation

measurements made with a handheld optical sensor. Meas-

urements made above and below the canopy are used to

determine canopy light interception from five angles, from

which LAI can be computed using a model of radiative

transfer in vegetative canopies. This approach assumes that

foliage is randomly distributed (LAI-2000 Plant Canopy

Analyser instruction manual, 1992) which is nearly true of

grassland. Direct sunlight conditions yield an underestima-

tion of LAI proportional to the amount of foliage exposed

to sunlight (LAI-2000 Plant Canopy Analyser instruction

manual, 1992). Even on cloudy days, a considerable

amount of direct sunlight generally reaches the surface

and variations in surface irradiance due to varying cloud

cover also introduce measurement errors. In the Sahel

environment, daily measurement is therefore restricted to

about 30 min around sunset and sunrise. Limited daily

time availability makes it impossible to obtain in situ

measured LAI over the area corresponding to a 1000-m

MODIS pixel. As an alternative, accurate LAI measure-

ments were carried out for an area corresponding to a 30-

m Landsat pixel, and Landsat ETM scenes covering the

site of in situ measurements were subsequently used for

heterogeneity analysis as described in Determining repre-

sentativeness of in situ sites to MODIS 1 km data using

Landsat ETM+ images.

LAI measurements were carried out along two diagonals

corresponding to the 30-m Landsat pixel covering the site of

the aluminium masts. Measurements were made for a

number of predefined locations along the two transects,

and the final LAI value for a given day is an average of

three consecutive runs of LAI measurements. A given set of

LAI runs should ideally produce the same results, but due to

measurement uncertainty, this never happens. The variation

between the smallest and largest measured LAI value has

been used for quantification of the measurement uncertainty.

It was found that the deviation between min and max for

LAI values above 0.4 in the worst case was 28% and, as an

average, the deviation was found to be 15%. For smaller

LAI values, the deviation in percent occasionally gets larger

than 28%, but the actual LAI deviation in absolute numbers

is limited.

4.2. In situ measurements of fAPAR

fAPAR is calculated from the difference in energy

entering the canopy and energy leaving the canopy (Begue

et al., 1991) divided by incoming PAR:

fAPAR ¼ ðPARi � PARcrÞ � ððPARtrð1� asÞÞPARiÞPARi

ð1Þ

where PARi is incoming PAR, PARcr is reflected PAR

from the canopy and as is the soil albedo (derived over

http:\\edcdaac.usgs.gov\landdaac\tools\modis\index.asp

Fig. 4. In situ measured fAPAR daily variations (10-min values) for a

cloud-free day (values corresponding to MODIS overpass and statistics

covering 9:00 am to 3:00 pm are highlighted).


bare soil using the two PAR quantum sensors). The

transmittance of PAR through the canopy can be described

by a Poisson probability function:

PARtr ¼ e�G�LAIsinb

where b is solar elevation and G is the mean cosine

direction between the solar zenith angle and the leaf

normal. The Poisson probability function does not account

for the multiple scattering contribution; detailed treatment

of scattering in the PAR wavelength is, however, not

critical because leaf absorption is high (Norman, 1979).

G takes the value of 0.5 for all solar elevation angles,

reflecting the assumption that the leaf inclination angles

are distributed uniformly over the surface of a sphere

(Baldocchi, 1993). The assumption of spherical leaf angle

distribution is sound for millet (Begue, 1994), but not for

grass and groundnut characterised by an erectophile and

planophile leaf orientation, respectively. Therefore, values

of G for varying solar zenith angles are calculated from

GðwÞ ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffix2cos2w þ sin2w

p

xþ 1:774ðxþ 1:182Þ�0:733

characterising an ellipsoidal leaf angle distribution, which

generalizes the spherical, but allows the sphere to be

flattened or elongated (Campbell & Norman, 1998). Here,

w is the solar zenith angle and x is the ratio of average

projected areas of canopy elements on horizontal and

vertical surfaces. Based on the literature, x is set to 0.8

for grasses, 1 for millet and 1.3 for groundnuts (Azam-Ali

et al., 1994; Campbell & Norman, 1998; Ross, 1981).

Daily averages of fAPAR are calculated from 10-min

sampling intervals between 9:00 am and 3:00 pm, and

averaging over such a large range of solar zenith angles

renders error from choice of G function negligible, as

shown by sensitivity studies performed by the authors.

Ideally, MODIS fAPAR data should be compared to 10:30

am in situ measurements; however, using instantaneous in

situ measurements for comparison introduces considerably

noise due to subpixel clouds. On a diurnal basis, fAPAR

was found to be highest in the morning and late afternoon

due to large solar zenith angles and lowest around noon

where the solar zenith angle is low (Fig. 4). It was

therefore found that daily averages of fAPAR calculated

from 9:00 am to 3:00 pm approximated the 10:30 am and

10:30 pm values as illustrated in Fig. 4. An in-depth

description of diurnal fAPAR variations as a function of

the bidirectional reflectance distribution function (BRDF)

will be the subject of a forthcoming paper.

The relationship between fAPAR and LAI expressed

through a Poisson probability function has previously been

studied and applied by Daughtry et al. (1992) and Turner

et al. (2002). Modelling the transmitted PAR from the LAI

measurements described above is preferred to direct meas-

urements of PAR transmitted through vegetation because

of the difficulties associated with measuring PAR below

plants. In a sparsely vegetated grass savannah, it is very

difficult to install PAR sensors below the vegetation

without influencing the plants. In order to measure average

radiation conditions below a partial canopy like that

offered by most crops, it would furthermore be necessary

to include numerous sensors in the below-canopy instru-

mental setup which was not possible in the framework of

the instrumental setup.

The measurement error on in situ measured fAPAR has

been assessed by sensitivity analysis of the model calcula-

tion. There are several factors potentially influencing the

precision of the fAPAR estimate. The uncertainty in mea-

suring LAI also influences the fAPAR; however, varying

LAI between max and min as measured for a given date (see

In situ measurements of LAI) produce fAPAR uncertainties

only half as large as on LAI itself (i.e., 7.5%). Other factors

influencing the fAPAR estimate are variations in soil albedo,

model assumptions of radiation transmittance and variations

in solar zenith angle as illustrated in Fig. 4. Based on the

model sensitivity analysis, the estimated uncertainty on in

situ measured fAPAR is found to be 15% (as it was the case

for LAI).

4.3. Determining representativeness of in situ sites to

MODIS 1-km data using Landsat ETM+ images

When comparing in situ measurements of LAI and

fAPAR with a MODIS 1000-m pixels, information from

Landsat 30-m pixels provides a useful tool for scaling

from point to area (Myneni et al., 2002; Tian et al., 2002).

By comparing the NDVI value for the Landsat pixel

covering the site of in situ measurements with the sur-

rounding Landsat NDVI pixels encompassed by the

corresponding MODIS 1-km pixel, it is possible to eval-

uate whether in situ measured vegetation intensity is

representative of vegetation intensity in the MODIS 1-km

Table 3

NDVI values of Landsat pixels covering the site of in situ measurements

and statistics of the 1111 pixels encompassed by the 1-km MODIS pixel

covering the location of in situ measurements; Grass savanna 2002

Fieldwork

site

NDVI statistics 01/08

2002

17/08

2002

04/10

2002

Dahra Landsat pixel value

for fieldwork site

0.150 0.210 0.180

Mean of area 0.144 0.203 0.176

Standard deviation

of area

0.009 0.012 0.011

Minimum of area 0.120 0.140 0.140

Maximum of area 0.180 0.260 0.250

Tessekre

North

Landsat pixel value

for fieldwork site

0.150 0.120 0.220

Mean of area 0.146 0.123 0.225

Standard deviation

of area

0.011 0.014 0.027

Minimum of area 0.120 0.090 0.180

Maximum of area 0.210 0.240 0.420

Tessekre

South

Landsat pixel value

for fieldwork site

0.160 0.140 0.350

Mean of area 0.162 0.138 0.342

Standard deviation

of area

0.009 0.013 0.033

Minimum of area 0.130 0.100 0.210

Maximum of area 0.210 0.230 0.410


pixel. Table 3 provides statistics of the three sites: the

NDVI value of the Landsat pixel covering the site of in

situ measurements and min, max, mean and standard

deviation of the 1111 Landsat pixels encompassed by the

MODIS 1-km pixel, respectively. The analysis of Landsat

NDVI pixel variance demonstrates that the areas around

the study sites are, in general, extremely homogenous with

respect to vegetation intensity. In all cases, the single

Landsat pixel NDVI value covering the sites of in situ

measurements is very close to the overall average of the

1111 pixels, and interpixel variance is very low revealing

that the vegetation density in the area in fact is very

homogeneous as also seen in Fig. 3. Based on this

heterogeneity analysis, the sites of in situ measurements

are found to be directly comparable to MODIS 1-km

satellite data. A detailed examination of the relation

between NDVI derived from in situ, Landsat and MODIS,

respectively, is reported in Fensholt and Sandholt (submit-

ted for publication).

5. Results and discussion

5.1. Comparison of in situ LAI measurements and MODIS

LAI

The 2001 and 2002 time series of LAI are pictured in

Fig. 5. The consequence of limited rainfall in 2002 is

clearly seen in the difference on the level of LAI at Dahra

between 2001 and 2002. Eight-day MODIS LAI values are

presented with their corresponding nonzero quality control

(QC) flags, which are explained in Table 4. It can be seen

that in two cases (QC flag 2), it was not possible to

produce a LAI value from the standard algorithms, so a

backup algorithm using MODIS NDVI is used instead.

This backup algorithm ensures a LAI (and fAPAR) value

for each composite period, though the quality of the

underlying NDVI pixel is also questionable because of

cloud interference. To facilitate direct comparison between

8-day MODIS values and in situ measured values, curves

are fitted to the MODIS 8-day data and daily data

interpolated. This was done using a four-parameter Gauss-

ian fit in the TableCurve software version 4 (www.systat.

com/products/TableCurve2D). The values of r2 are gener-

ally high justifying the comparative approach; however, for

2001, the r2 is lower presumably due to the September 6

LAI value, a possible outlier. This value is assigned QC

flag 2, signifying that it originates from a MODIS NDVI

value and is probably influenced by clouds.

The seasonal dynamics of LAI are captured reasonably

well, as can be seen when comparing the in situ data points

to the MODIS data in Fig. 5. Vegetative growth was much

more intense in 2001 than 2002: even the modest level of

vegetation intensity characterising the start of growing

season 2001 is never reached in 2002. As can be seen in

Fig. 6A, in situ and MODIS LAI values agree quite well

(LAI values of 1.5–2.5) with a slope close to the 1:1 line

and r2 value of 0.81. MODIS LAI values are slightly

overestimated with values 0.03–0.2 above in situ measure-

ments. This level is close to the overall uncertainty of the in

situ measured LAI, with several points lying within the

limits of the error bars. One value is, however, 0.4 larger,

and this range of values corresponds to an overestimation of

approximately 2–15%.

Scale dependency on the LAI measurements influences

the overall level of the values. The probability of spatial

variability in the soil background reflectance is larger for

coarse resolution pixels compared to fine resolution pixels.

When the spatial resolution of satellite data is decreased,

pixels are therefore likely to contain an increased amount

of radiation reflected from the background, causing an

underestimation of LAI (Tian et al., 2000). The magnitude

of underestimation also increases as vegetation heteroge-

neity increases, and Tian et al. (2002) found a 1-km

MODIS LAI pixel to be underestimated by 5% compared

to field measurements if resolution of the data is not

considered in the retrieval technique. The site in Botswana

is, however, more heterogeneous (with woodland and

adjacent shrubland patches) than the sites in Senegal and

the heterogeneity influence is therefore not expected to be

pronounced in this study. The 2001 MODIS LAI values

are, however, marginally higher than the in situ measured

data as indicated by the regression intercept of 0.13. The

highest in situ value of LAI in 2002 for the three sites is

just below 1 and the agreement between in situ measure-

ments and MODIS data are of varying quality (Fig. 6B–

D). Because of the very low dynamic range of data, it is,

http:\\www.systat.com\products\TableCurve2D

Fig. 5. Time series of in situ measured LAI, MODIS 8-day LAI and daily MODIS LAI modelled (interpolated) from 8-day MODIS values. MODIS LAI quality

flags are inserted (explained in Table 4). Fifteen percent error bars are added to the in situ measured LAI values.


however, expected that the slope/offset varies. Averaging

all sites (Fig. 6E) yields an offset of 0.33 and a slope of

0.92. The r2 value is 0.92 indicating an overall good

agreement between in situ measurements and MODIS

LAI. Averaging all sites is justified since all sites are

Table 4

The MODIS fAPAR/LAI and NDVI Collection 4 quality flags

MODIS

LAI/fAPAR

quality flag

Description MODIS NDVI

quality flag

Description

0 Highest overall

quality

1 High quality

1 Good quality 2 Good quality

2 Not produced,

cloud

3 Acceptable quality

3 Not able to

produce

4 Fair quality

5 Intermediate quality

6 Below intermediate

quality

7 Average quality

8 Below average

quality

9 Questionable quality

similar regarding vegetation type, structure (LAD) and tree

cover. The sites are all classified as MODIS12 LAI/fAPAR

land cover type 2 (shrubs) so between-site differences

cannot be explained by different biome-specific algorithms

used for MODIS LAI/fAPAR.

However, Figs. 5 and 6 indicate that the base level of

MODIS LAI is too high in the dry season. The regression

equation offset in the last scatterplot of Fig. 6 representing

all data from 2001 and 2002 indicates that MODIS dry

season LAI is above 0.3 which discords with dry season in

situ measurements of nearly 0. By the end of the dry

season, the vegetation cover is absent and the annual

grasses from the previous growing season have been used

as forage (Fig. 7). MODIS dry season LAI should then be

a function of tree cover, which generally is around 2% of

the area. Even if the LAI of a 5-m B. aegyptiaca tree is set

between 4 and 5, MODIS LAI should still not exceed 0.1.

Furthermore, both B. aegyptiaca and B. senegalensis are

semideciduous; therefore, the dry season MODIS LAI

values cannot be explained by tree cover. The quality of

the retrievals cannot be better than the quality of the

largest uncertainty in the model and spectral reflectance

data input to the algorithm (as described in Satellite data).

The uncertainties due to MOD12 biome scheme misclas-

Fig. 6. Scatterplots of in situ measured LAI and daily MODIS LAI interpolated from 8-day values. (A) Dahra 2001, (B) Dahra 2002, (C) Tessekre North 2002,

(D) Tessekre South 2002 and (E) all sites. Fifteen percent error bars are added to the in situ measured LAI values.


sification and/or biome mixtures within 1-km pixel can be

assumed negligible because of the homogeneous areas of

fieldwork with respect to vegetation type and number of

Fig. 7. Photograph of the Dahra site 7/5 2001. The picture illustrates the fieldwork

452412; 1699018, zone 28).

species. Moreover, in case of misclassification between

biomes characterised by distinctly different structure, the

potential error in retrieved LAI and fAPAR can be large,

equipment mounted on a grass savannah in the end of the dry season (UTM:


but the biomes represented in this area (shrubs; biome 2

and savannas; biome 4) are spectrally and structurally

similar biomes, meaning that the retrieved LAI/fAPAR

values are acceptable even though a given pixel has been

misclassified (Myneni et al., 2002). Uncertainty due to

saturation problems of the retrieved values because of

dense vegetation is not likely to be encountered in sparse

biomes such as grasses and shrubs (Myneni et al., 2002).

Uncertainties in the surface reflectance product, however,

set a limit to the minimum LAI retrievable from MODIS

satellite data. Variations in surface reflectance due to varia-

tions in LAI must exceed the variations originating from

uncertainties in surface reflectance to be treated as reliable

in the LAI/fAPAR algorithm. The uncertainty in the input

data was described in Satellite data. Wang et al. (2001)

estimated the dispersion on LAI to approximately 0.10–

0.15 for two distinctly different biomes, and from this

MODIS LAI, values will therefore never be smaller than

0.1 even if no vegetation is present. The dry season values

around 0.3 are, however, slightly larger than the a priori

minimum LAI value.

Few studies validating MODIS LAI are currently avail-

able. Privette et al. (2002) suggest a good agreement

between MODIS LAI and in situ measured LAI for semi-

arid woodland and savannah in Southern African Kalahari.

Fernandes et al. (2002) found the Collection 3 MODIS LAI

Fig. 8. Time series of in situ measured fAPAR, MODIS 8-day fAPAR and daily M

fAPAR quality flags are inserted (explained in Table 4). Fifteen percent error bar

to be 33% higher than Landsat-derived LAI, calibrated with

in situ LAI over the Boreas region of Canada. The overall

pattern of this study is that the MODIS LAI captures in situ

measured LAI in Western Africa (biome 2; shrubs) fairly

accurate.

5.2. Comparison of in situ fAPAR and MODIS fAPAR

measurements

Time series of in situ fAPAR calculated from Eq. (1) are

compared to MODIS fAPAR in Fig. 8. MODIS fAPAR

values are assigned QC flags using the same standards as for

LAI (QC flags are explained in Table 4). The 8-day fAPAR

values have been interpolated to yield daily values directly

comparable with the in situ measured values. As with LAI,

the seasonal trajectory of fAPAR is best described by a four-

parameter Gaussian fit and the model explains the variation

in the 8-day MODIS fAPAR data to satisfaction (r2 values

from 0.72 to 0.98). The time series for Tessekre South

covers only the beginning of the growing season due to

instrument failure.

Fig. 9 shows scatterplots of in situ fAPAR versus daily

MODIS fAPAR interpolated from the 8-day values. The

seasonal variation of the in situ measurements is generally

captured very well by the MODIS fAPAR product, as

indicated by the high r2 values (r2 values from 0.81 to 0.98).

ODIS fAPAR modelled (interpolated) from 8-day MODIS values. MODIS

s are added to the in situ measured fAPAR values.

Fig. 9. Scatterplots of in situ measured fAPAR and daily MODIS fAPAR interpolated from 8-day values. (A) Dahra 2001, (B) Dahra 2002, (C) Tessekre North

2002, (D) Tessekre South 2002 and (E) all sites. Fifteen percent error bars are added to the in situ measured fAPAR values.


Dahra had high vegetation intensity in 2001 and Figs.

8 and 9 show that MODIS fAPAR captures the seasonal

variation in in situ measurements well. The overall level of

MODIS fAPAR is, however, moderately overestimated. A

study by Huemmrich et al. (in press) for Kalahari wood-

land in Zambia likewise finds MODIS fAPAR to be

overestimated even though the MODIS LAI was found

to correspond well to in situ measured LAI. The dynamic

range of the 2002 data is narrow because of the very

limited rainfall and resulting short growing season and

poor vegetation development. The three sites exhibit some

variation in the slope values since all sites cover a limited

range of vegetation densities. The plot comprising all sites

(Fig. 9E) illustrates the wide range of fAPAR values

covered by the in situ measurements. The in situ measured

fAPAR is captured well by MODIS fAPAR with an r2

value of 0.92. For all four data series, the overall level of

the MODIS fAPAR values is higher than in situ fAPAR

with all points above the 1:1 line and an offset of 0.22.

The highest MODIS fAPAR values are overestimated by

0.06–0.15 (approximately 8–20%) which is a larger

overestimation than it was the case for MODIS LAI. As

it was the case for MODIS LAI, the offset moderately

exceeds the uncertainty attributed to the input data.

MODIS fAPAR never drops below 0.2, which is somewhat

higher than the input channel uncertainty levels, which, by

Wang et al. (2001), can explain a fAPAR offset of

approximately 0.1.

5.3. Relation between in situ measured fAPAR and NDVI

The robustness of the relation between fAPAR and NDVI

for different types of vegetation has been tested on in situ

measured time series of both parameters. When a canopy

enters the phase of senescence, the change in PAR absorp-

tion will be larger than absorption change in the NIR bands

used for NDVI (Huemmrich et al., in press; Le Roux et al.,

1997; Van Leeuwen & Huete, 1996), thereby fundamentally

altering the relation. The data presented are thus only from

the vegetative phase which is also the phase of interest when

modelling NPP.

As described in Introduction, numerous authors have

studied the relation between fAPAR and NDVI using

satellite data or radiative transfer models and there is

general agreement that the relationship is linear for green

vegetation. However, few studies have verified this rela-

tionship empirically by in situ measurements of different

canopy types or whether the relation is constrained by

variations in view angle geometry and/or canopy-related

conditions like LAD, canopy heterogeneity, soil–canopy

Fig. 10. Scatterplots of in situ measured fAPAR and NDVI for three different vegetation types characterised by different LAD and canopy heterogeneity:

annual grasses, millet and groundnut 2001 and 2002. All data are from the growing phase. Fifteen percent error bars are added to the in situ measured fAPAR

values.



reflectance interactions and senescent material in the can-

opy. Scatterplots of in situ fAPAR and NDVI are presented

in Fig. 10. For 2 years, systematic in situ measurements

were carried out for three different vegetation types—

annual grasses, millet and groundnut—all characterised

by different LAD and canopy heterogeneity. The grass

savannah is very homogenous and categorized by an

erectophile leaf angle distribution, whereas the LAD of

groundnuts is planophile with bare patches of soil in

between the plants. Millet has spherical LAD but is also

cultivated with bare patches of soil in between the indi-

vidual plants. Each point in the scatterplots represents a

daily averaged fAPAR and NDVI value and for each type

of vegetation, a wide range of vegetation intensities was

measured.

From the scatterplots in Fig. 10, a linear relation between

fAPAR and NDVI is evident for all vegetation types. The

regression parameters are almost constant (with a slope of

1.5 and an offset of � 0.4) and r2 values are high (ranging

between 0.85 and 0.98). For all vegetation types, the

number of sample points and the distribution with respect

to vegetation intensity support the existence of a solid linear

relation. This contradicts the findings of Le Roux et al.

(1997) (Table 5) but agrees well with the measurements of

Asrar et al. (1984), Hatfield et al. (1984), Begue (1991) and

Lind and Fensholt (1999) where a linear relation for

different crop types was found (all Table 5). The regression

slopes found by those authors differ from the slopes found

here, but it is difficult to compare absolute values of in situ

NDVI because the signal depends on the wavelength

configuration of the radiometer. The last scatterplot com-

Table 5

fAPAR/NDVI relations found in the literature for comparison

Reference Relationship Vegetation

type

Experimental

condition

Begue (1991) fAPAR= 1.39�NDVI� 0.13

Millet In situ

Lind and

Fensholt

(1999)

fAPAR= 1.42�NDVI� 0.39

Millet, grass,

sorghum

In situ

Asrar et al.

(1984)


Spring wheat In situ

Hatfield et al.

(1984)


Spring wheat In situ

Le Roux et al.

(1997)

fAPAR= 0.393�2.361�NDVI +

3.782�NDVI2

Savanna grass In situ

Myneni and

Williams

(1994)


Various input Radiative transfer

model (SAIL)

Prince and

Goward

(1995)

fAPAR= 1.62�NDVI� 0.036

Mixed AVHRR satellite

data (not

atmospheric-

corrected)

Ruimy et al.

(1994)

fAPAR= 1.25�NDVI� 0.025

Mixed AVHRR satellite

data (atmospheric-

corrected)

prises all the data to test whether the linear relations found

for the different vegetation types are dependent on canopy

and site-specific conditions. A strong linear relation is still

found (r2 value of 0.92) between fAPAR and NDVI sug-

gesting that the linear relationship is insensitive to variations

in LAD and canopy heterogeneity. This insensitivity was

also suggested by Lind and Fensholt (1999) from measure-

ments of millet and a limited number of samples of

savannah grass and sorghum (Table 5). The results agree

with findings by Goward and Huemmrich (1992) and by

Myneni and Williams (1994) (Table 5); both studies were

based on radiative transfer models.

It is likely that soil colour variations influence the

relation because darker soils tend to increase NDVI and

vice versa, whereas the influence of soil colour on fAPAR

is minimal (Huete et al., 1999). Though dependency on

soil background is not detectable from these results, it

must be stressed that the test sites do not represent a large

variability regarding soil type and colour. In situ measure-

ments of fAPAR and NDVI covering a broader range of

soil types are required to test the sensitivity of the

relationship to soil colour variations. Nonetheless, a study

by Fensholt (in press) using MODIS satellite data for the

whole of Senegal indicates that variations in the relation

between fAPAR and NDVI as a consequence of varying

soil colour are limited.

5.4. Relation between satellite-measured fAPAR and NDVI

MODIS fAPAR/MODIS NDVI relations for the pixels

covering the sites of fieldwork are given in Fig. 11. A

simple maximum value composite of two fAPAR 8-day

periods is performed to enable comparison with 16-day

NDVI data, and the QC flags from the MODIS 16-day

CV-MVC product are included in each plot; the quality

notation is explained in Table 4.

The r2 values are generally high (ranging from 0.76 to

0.95), confirming the linear relation found by in situ

measurements. The regression slopes and offset are fairly

constant; slopes vary from 0.95 to 1.06 and offset from

� 0.03 to 0.04 with regressions including suboptimal

quality pixels influenced by cloud cover, as indicated

by the QC flags. However, when clouds are present,

MODIS fAPAR value is derived from the backup routine

using MODIS NDVI and therefore both products are

suppressed uniformly. The last scatterplot in Fig. 11

includes data characterised by QC flags 1 and 2 from

all sites and the r2 value is 0.89, indicating a strong linear

relation.

5.5. Comparing in situ and satellite-derived relations

between fAPAR and NDVI

Numerous studies have derived fAPAR from NDVI by

empirical relations between fAPAR and NDVI as mentioned

in Introduction. Earlier studies of AVHRR images have

Fig. 11. Scatterplots of 16-day MODIS fAPAR and 16-day MODIS MVC NDVI with MODIS CV-MVC NDVI quality flags inserted (explained in Table 3).


found considerable differences between in situ and remotely

sensed data in terms of the relation between fAPAR and

NDVI. This has, however, primarily been due to signal

attenuation by aerosols and atmospheric water vapour on the

NDVI signal vis-a-vis in situ measurements (Goward et al.,

1991; Lind & Fensholt, 1999). Regression between fAPAR

and NDVI derived from the AVHRR Pathfinder data by

Prince and Goward (1995) (Table 5) yielded a higher slope

because of the limited dynamic range captured by the

Pathfinder (due to the influence from atmosphere), a prob-

lem largely overcame by MODIS. Correcting AVHRR data

for atmospheric influence (Ruimy et al., 1994) yields a

regression (Table 5) closer to the MODIS satellite regres-

sions in Fig. 11.

The in situ and satellite-based relation between MODIS

fAPAR and NDVI data presented in this paper are directly

comparable because the wavelengths used to calculate in

situ fAPAR and NDVI are similar to the wavelengths used

by the MODIS satellite. The regression slopes found from

the MODIS satellite-based relations between fAPAR and

NDVI (Fig. 11) are lower than that obtained from in situ

measurements (Fig. 10). This gels with the fAPAR analysis

in Fig. 9, where MODIS fAPAR was consistently higher

than in situ fAPAR. A study by Fensholt and Sandholt

(submitted for publication) reveals a high level of agree-

ment between MODIS and in situ NDVI, indicating that

the discrepancy between the MODIS fAPAR/NDVI regres-

sion slope and the in situ fAPAR/NDVI regression slope


only to a limited extent can originate from the NDVI data.

It must, however, be stressed that even thoughMODISNDVI

values are based on atmospherically corrected data, the

sensor view angles in a 16-day composite image may still

vary considerably which introduces instability in the assess-

ment of fAPAR fromNDVI. The relation is also dependent on

solar zenith angle as illustrated in Fig. 4; even though fAPAR

and NDVI respond similar to diurnal solar zenith variations,

the different sensitivity makes the relation dependent on the

solar zenith angle. Despite the apparent insensitivity of the

fAPAR/NDVI relation to LAD, the BRDF thus introduces

instability in the assessment of fAPAR from NDVI. The

satellite-derived fAPAR/NDVI relations found here and the

relation to the in situ measured fAPAR and NDVI data

therefore do not apply on a global scale but is only valid

for similar sun-sensor BRDF.

6. Conclusion

At three different sites in semi-arid Senegal, West Africa,

in 2001 and 2002, MODIS LAI and fAPAR products have

been evaluated against in situ measurements. All sites are

characterised by a homogeneous cover of annual grasses

and a tree cover of maximum 2%. The seasonal dynamics of

both in situ LAI and fAPAR was captured fairly accurately

by MODIS LAI (r2 values of 0.23–0.96) and fAPAR (r2

values of 0.81–0.98). Both products were characterised by a

moderate offset, LAI having an offset of approximately 0.3

and fAPAR an offset of approximately 0.2–0.25. These

values indicate dry-season MODIS LAI and fAPAR base

levels which conflicts with actual dry-season field condi-

tions, where grass vegetation is all absent. The overall level

of the MODIS fAPAR values is higher than in situ fAPAR

with all points above the 1:1 line. The highest MODIS

fAPAR values from 2001 are overestimated by 0.06–0.15

(approximately 8–20%) which is a larger overestimation

than was the case for MODIS LAI (0.03–0.4, approximate-

ly 2–15%). These findings apply only to the LAI/fAPAR

biome class 2 (shrubs); additional studies are needed in

other regions characterised by same biome class, as well as

for other biomes, to determine to what extent offsets and

overestimation of fAPAR apply in other regions and biomes.

In situ measurements of fAPAR and NDVI (calculated

using the same wavelengths as MODIS NDVI) were con-

ducted for three different vegetation types during the field

campaign 2001 and 2002. Measurements were taken at

various phenological stages to cover a range of vegetation

intensities, and the three vegetation types were characterised

by different leaf angle distributions and homogeneity:

erectophile and homogeneous grasses, spherical and

clumped millet and planophile and clumped groundnut. A

strong linear relation was found between fAPAR and NDVI

(r2 ranging from 0.85 to 0.98) for each vegetation type.

Plotting all types together changed neither the coefficients

of explanation nor the regression parameters, confirming

that the relation between fAPAR and NDVI is insensitive to

variations in LAD and heterogeneity.

From MODIS data, a linear relation was also found

between fAPAR and NDVI. However, the regression coef-

ficients differ from those characterising the in situ data

because of the offset in the MODIS fAPAR data. The

fAPAR/NDVI relation insensitivity to LAD enables simple

empirical modelling of fAPAR from satellite NDVI; how-

ever, it must be stressed that such empirical relations are

dependent on solar and view zenith angle geometry and soil

colour variations. The satellite-derived fAPAR/NDVI rela-

tions found here and the relation to the in situ measured

fAPAR and NDVI data therefore do not apply on a global

scale but is only valid for similar sun-sensor view geometry

and soil colour.

Validation of MODIS fAPAR and LAI is an ongoing

process. Results from the fluxnet validation sites and sub-

sites around the world will be used to adjust and refine

MODIS fAPAR and LAI, thereby approaching real-world

levels for all biomes and continents. The current study

suggests that MODIS LAI reproduce the real-world LAI

for the semi-arid Senegal quite accurately, whereas MODIS

fAPAR is overestimated.

Acknowledgements

The study is funded by the Danish Research Councils,

ESA-related research, grant no. 9902490. Scientific equip-

ment for fieldwork is funded by the Danish Research

Agency, Danish Agricultural and Veterinary Research

Council grant no. 23-01-0153. The authors would like to

thank the MODIS Land Discipline Group for creating and

sharing the MODIS LAND data sets. The Centre de Suivi

Ecologique in Dakar is warmly acknowledged for providing

logistic support. The authors would also like to thank the

staff at Institut Senegalais de Recherhes Agronomiques

(ISRA) in Dahra for support during the fieldwork. Also,

Jørn Torp Pedersen, Mette Wolstrup Pedersen and Anette

Nørgaard deserve thanks for their fieldwork assistance.

Finally, we thank the anonymous reviewers for their sug-

gestions for improving this paper.

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Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment...

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