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Remote Sensing of Environment 87 (2003) 111–121

Derivation of a shortwave infrared water stress index from MODIS

near- and shortwave infrared data in a semiarid environment

Rasmus Fensholt*, Inge Sandholt1

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

Received 11 February 2003; received in revised form 30 June 2003; accepted 4 July 2003

Abstract

Two different configurations of a shortwave infrared water stress index (SIWSI) are derived from the MODIS near- and shortwave

infrared data. A large absorption by leaf water occurs in the shortwave infrared wavelengths (SWIR) and the reflectance from plants thereby

is negatively related to leaf water content. Two configurations of a water stress index, SIWSI(6,2) and SIWSI(5,2) are derived on a daily basis

from the MODIS satellite data using the information from the near infrared (NIR) channel 2 (841–876 nm) and the shortwave infrared

channel 5 (1230–1250 nm) or 6 (1628–1652 nm), respectively, which are wavelength bands at which leaf water content influence the

radiometric response. The indices are compared to in situ top layer soil moisture measurements from the semiarid Senegal 2001 and 2002,

serving as an indicator of canopy water content. The year 2001 rainfall in the region was slightly below average and the results show a strong

correlation between SIWSI and soil moisture. The SIWSI(6,2) performs slightly better than the SIWSI(5,2) (r2 = 0.87 and 0.79). The

fieldwork in 2002 did not verify the results found in 2001. However, year 2002 was an extremely dry year and the vegetation cover

apparently was too sparse to provide information on the canopy water content. To test the robustness of the SIWSI findings in 2001, soil

moisture has been modelled from daily rainfall data at 10 sites in the central and northern part of Senegal. The correlations between SIWSI

and simulated soil moisture are generally high with a median r2 = 0.72 for both configurations of the SIWSI. It is therefore suggested that the

combined information from the MODIS near- and shortwave infrared wavelengths is useful as an indicator of canopy water stress in the

semiarid Sahelian environment.

D 2003 Published by Elsevier Inc.

Keywords: Shortwave infrared water stress index (SIWSI); MODIS satellite data; Rainfall; Soil moisture; Vegetation index; Sahel

1. Introduction

In the drylands of the Sahelian zone in West Africa,

management of vegetation resources is of key importance.

Since the 1970s, this zone has been characterized by erratic

climatic changes/fluctuations having profound impacts on

the natural ecosystems and the agricultural production.

Proper management of natural resources in this zone

requires regular information on the seasonal development

and variation in a range of biophysical parameters but

because of limited availability of monitoring network in

these areas it is a difficult task. Considering the varying

spatial and temporal range of these biophysical parameters,

it is evident that the use of repetitive accurate Earth

0034-4257/$ - see front matter D 2003 Published by Elsevier Inc.

doi:10.1016/j.rse.2003.07.002

* Corresponding author. Tel.: +45-35322526; fax: +45-35322501.

E-mail addresses: rf@geogr.ku.dk (R. Fensholt), is@geogr.ku.dk

(I. Sandholt).1 Fax: +45-35322501.

Observation (EO) data is the only realistic means of acquir-

ing much of this information. During the last decades, much

focus has been placed on the modelling of biophysical

variables from EO surface reflectance data aiming at mon-

itoring the productivity of terrestrial ecosystems (net prima-

ry production, NPP) and concurrently with the rapid

development of satellite sensor design the accuracy of the

derivation of biophysical variables have also improved

considerably. New satellite sensors like MODIS sensor,

flying aboard NASA’s TERRA satellite and the MERIS

carried by the European ENVISAT have been substantially

improved on the subject of spectral and spatial resolution

compared to the widely used NOAA AVHRR.

When modelling net primary production (NPP) in a

semiarid environment like the Sahelian zone canopy, water

stress is a key variable. NPP has traditionally been assessed

using the light use efficiency approach (LUE) but without

information on canopy water stress the LUE approach

provides an estimate of potential rather than actual produc-

R. Fensholt, I. Sandholt / Remote Sensing of Environment 87 (2003) 111–121112

tion. Canopy water stress has proved to be difficult to derive

from conventional EO data. However, the spectral config-

uration on the MODIS sensor open up the possibilities of

the derivation of a shortwave infrared water stress index

(SIWSI) on a daily basis using the information from the

shortwave infrared channel 5 (1230–1250 nm) or 6 (1628–

1652 nm), which are wavelength areas at which leaf water

content influence the radiometric response. In this paper,

two different configurations of the SIWSI are derived from

the MODIS near- and shortwave infrared data. The indices

are compared to in situ top layer soil moisture measurements

from the semiarid Senegal 2001 and 2002, serving as an

indicator of canopy water content. Soil moisture is further-

more modelled from daily rainfall data to be able to test the

SIWSIs against soil moisture for a larger number of loca-

tions than the fieldwork sites.

2. Background

A summary of the approaches on the subject of plant

water stress monitoring is provided starting with a brief

review on the development of the NPP assessment to

underline the importance of a water stress estimate within

the scientific domain of modelling NPP in a semiarid

environment.

The net primary production provides a measure of the

production activity or growth of terrestrial vegetation.

Estimation of NPP is often based on the LUE concept

originally proposed by Monteith (1972) who considered

biomass accumulation as an ongoing process correlated with

the amount of photosynthetically action radiation (PAR)

absorbed or intercepted by green foliage (APAR). The LUE

approach however provides an estimate of potential produc-

tion rather than the actual production and the applicability of

modelling plant production using this approach depends on

the possibilities of taking into account variations in the

physical environment regarding plant stress arising from

temperature, light, lack of water and nutrients, and proper

modelling of respiration for maintenance and growth

(Goetz, Prince, Goward, Thawley, & Small, 1999; Prince

& Goward, 1995). In semiarid areas like the Sahelian zone,

rainfall is considered to be the limiting parameter for

vegetation growth (Diallo, Diouf, Hanan, Ndiaye, & Pre-

vost, 1991; Hendricksen & Durkin 1986; Hielkema, Prince,

& Astle, 1986; Malo & Nicholson, 1990). The need for

implementation of information on canopy water stress in the

NPP models is therefore evident. A number of studies have

focused on the derivation of information on plant water

stress and the majority of these can be divided into two

different theoretical approaches.

2.1. Near infrared and thermal infrared

The concept of using information from the thermal

infrared wavelengths (TIR) to monitor canopy water stress

was originally proposed by Jackson, Reginato, and Idso

(1977) who developed the crop water stress index (CWSI).

Numerous studies have suggested that the combined infor-

mation from surface temperature (Ts) and NDVI in combi-

nation indirectly can provide information on vegetation

stress and moisture conditions at the surface. As a given

area dries, it is expected that the relationship between

NDVI and Ts when plotted as scatterplot will be altered

due to an increased Ts for the low NDVI areas (Nemani &

Running, 1989). The relation is often expressed by the

slope of a line fit to the Ts/NDVI dry edge. Nemani and

Running (1989) related the slope of the Ts/NDVI relation-

ship to the stomata resistance and the evapotranspiration of

a deciduous forest. Boegh, Soegaard, Hanan, Kabat, and

Lesch (1998) and Jiang and Islam (1999) relates the Ts/

NDVI slope to the surface evapotranspiration. Analysis of

the NDVI/Ts space has also been used to derive information

on regional soil moisture conditions (Carlson & Gillies,

1993; Goetz, 1997; Goward, Xue, & Czajkowski, 2002;

Sandholt, Rasmussen, & Andersen, 2002). Moran, Clarke,

Inoue, and Vidal (1994) combined the NDVI/Ts space

approach with standard meteorological data to form a water

deficit index (WDI).

2.2. Near infrared and shortwave infrared

Physically based radiative transfer models and laboratory

studies have shown that changes in water content in plant

tissues have a large effect on the leaf reflectance in several

regions of the 0.4–2.5 Am spectrum. It is well known that a

large absorption by leaf water occurs in these wavelengths

and therefore shortwave infrared reflectance (SWIR) reflec-

tance is negatively related to leaf water content (Bowman,

1989; Ceccato, Flasse, Tarantola, Jacquemoud, & Gregoire,

2001; Hunt, Rock, & Nobel, 1987; Tucker, 1980) and

increased reflectance in these wavelengths is the most

consistent leaf reflectance response to plant stress in general

including water stress (Carter, 1994). It then might be

possible to obtain a direct measure of vegetation water

content. The largest of these regions is the 1.3–2.5 Aminterval (SWIR) where the amount of water available in the

internal leaf structure largely controls the spectral reflec-

tance (Tucker, 1980). Moving from the demonstration of

plant water stress towards EO-based observation of canopy

water stress introduces some new difficulties. The main

challenge is to investigate if the changes introduced by

changing the scale will affect the relationships found on leaf

level. Reflectance from other sources will inevitably mix

with the signal from the plant when using satellite images.

The effect of soil background reflectance, leaf biochemical

parameters (cellulose and lignin), leaf internal structure, leaf

dry matter, canopy biophysical parameters (LAI) and the

influence from the atmosphere will all have an effect on the

satellite measured reflectance (Ceccato et al., 2001; Hunt &

Rock, 1989; Penuelas, Filella, Biel, Serrano, & Save, 1993;

Rollin & Milton, 1998) and the question is whether the

R. Fensholt, I. Sandholt / Remote Sensing of Environment 87 (2003) 111–121 113

effect of water content on plant reflectance is still distin-

guishable from these influences.

Tucker, 1980 considered simulated leaf reflectance

combined with the transmission properties of the atmo-

sphere and found out that the spectral region best suited

for remotely sensed leaf water content was from 1550 to

1750 nm. This spectrum is identical to the Landsat TM

channel 5 and numerous studies have been conducted

using Landsat TM data for water stress monitoring through

broad channel ratio and combination techniques using

Landsat TM channels 4 and 5. Several of these studies

concluded that the changes of canopy water status were

less than the variations due to other factors like changes in

canopy geometry (Cohen, 1991) while other studies using

the hyper spectral advanced visible infrared imaging spec-

trometer (AVIRIS) with a sampling interval of 10 nm

appears more promising (Gao, 1996; Serrano, Ustin, Rob-

erts, Gamon, and Penuelas, 2000).

3. Method

The method used in this study is a continuation of the

approach using information from the near- and shortwave

infrared wavelengths. The new MODIS satellite instrument

has the advantage of two narrow discrete channels in the

SWIR with a signal to noise ratio above 100 (Guenther,

Xiong, Salomonson, Barnes, & Young, 2002) which both

could be useful for monitoring of leaf water content (Gao,

1996). In Fig. 1, the MODIS bands 1–6 spectral config-

urations combined with information of the atmospheric

water vapour transmission function can be seen. It should

be noticed that all the MODIS bands are placed in atmo-

spheric windows where the influence from water vapour

(and aerosols) is minimized. Superimposed are the spectral

reflectance from a vegetated surface with two different leaf

water contents (low water content = 0.001, high water con-

tent = 0.03). It can be seen that the MODIS channels 5 and 6

are located in wavelength areas where the leaf water content

plays a decisive role on the leaf reflectance. This makes the

Fig. 1. The MODIS bands 1–6 spectral configuration combined with information o

spectral reflectance from a vegetated surface with two different values of water

atmospheric water vapour transmission; modified after Vermote and Vermeulen (

(2001).

MODIS instrument channels 5 and 6 well suited for canopy

water monitoring because of the plant water sensitivity in

these wavelengths combined with the high atmospheric

water vapour transmittance.

Canopy water content is not the only parameter respon-

sible for reflectance variations in the MODIS channels 5 and

6 (SWIR). Variations in leaf internal structure and leaf dry

matter content also influences the SWIR reflectance and

consequently SWIR reflectance values alone are not suitable

for retrieving vegetation water content. A method to isolate

the reflectance contribution due variations in leaf internal

structure and leaf dry matter content is to exploit reflectance

information from near infrared wavelength (NIR) from 700

to 900 nm. The NIR reflectance is affected by leaf internal

structure and leaf dry matter content but not by water

content. By combining the NIR reflectance information with

the SWIR reflectance information, variations induced by

leaf internal structure and leaf dry matter content can be

removed and thus improve the accuracy in retrieving the

vegetation water content (Ceccato et al., 2001).

Two configurations of the shortwave infrared water stress

index will be tested:

SIWSIð6;2Þ ¼ q6 � q2

q6 þ q2

ð1Þ

SIWSIð5;2Þ ¼ q5 � q2

q5 þ q2

ð2Þ

where q is the reflectance and the spectral range of MODIS

channel 2 is from 841 to 876 nm, channel 5 is from 1230 to

1250 nm and channel 6 is from 1628 to 1652 nm. The

SIWSIs are normalized indices and the values thereby

theoretically vary between � 1 and 1. An index value above

zero means that the reflectance from channel 6 is higher than

the channel 2 reflectance and this indicates canopy water

stress. A low index value is a consequence of a higher

channel 2 reflectance than channel 6 which indicates suffi-

cient quantities of water in the canopy for photosynthetical

activity. Some considerations on the phenology of the

vegetation are needed to be able to derive information on

f the atmospheric water vapour transmission function. Superimposed are the

thickness (low water content = 0.001, high water content = 0.03). Sources:

1999). Water thickness reflectance; modified after Zarco-Tejada and Ustin

Fig. 3. Mean normalized Sahel rainfall (June–October) from 1950 to 2000.

The averages are standardized such that the mean and standard deviation of

the series are 0 and 1 from the period 1898–1993. Source: National Center

for Atmospheric Research, World Monthly Surface Station Climatology.

R. Fensholt, I. Sandholt / Remote Sensing of Environment 87 (2003) 111–121114

water stress from the index. It is evident that the presence of

a certain amount of vegetation is needed for the index to be

meaningful. Furthermore, when the vegetation moves from

the vegetative phase into the reproductive phase finally

reaching the senescent stage very little energy in the

vegetation is used for photosynthesis but for respiration

and thereby the availability of water becomes less impor-

tant. Consequently the information derived from SIWSIs is

only useful in the vegetative phase when the vegetation has

reached a certain ground cover. It is well known that the soil

background has a large impact on the reflected signal from a

partially vegetated surface when calculating traditional VIs.

This is because the large reflectance/transmittance differ-

ence between the red and the near infrared wavelengths

stands out most clearly at 50% canopy cover (Huete, 1988).

In the present study, a minimum threshold value at which

the stress index works is being investigated using in situ soil

moisture measurements from sites of varying vegetation

cover. The SIWSIs are compared to in situ top layer soil

moisture measurements from the semiarid Senegal 2001 and

2002. Annual grasses are the dominating vegetation type at

all test sites and the water status of plants is directly related

to the soil moisture tension in the rooting zone (Ridder,

Stroosnijder, Cisse, & van Kelulen, 1983). The root biomass

of annual grasses in a semiarid environment is concentrated

in the upper 20 cm of the soil. Singh and Coleman (1975)

found that 68–78% of the total root biomass occurred

between 0 and 0.20 m on a short grass prairie. Cox, Frasier,

& Renard (1986) measured root biomass distribution on a

semiarid grassland and 73% of root biomass was found in

the first 0.15 m. Volumetric soil moisture content measured

in 10 cm thereby is regarded as a proxy of the water

availability for the plant photosynthetic activity.

Soil moisture is furthermore modelled from daily rainfall

data. Records of daily rainfall from 10 stations covering the

semiarid Senegal 2001 is used for soil moisture modelling to

be able to test the SIWSIs against soil moisture for a larger

number of locations than the fieldwork sites. A conceptual

Fig. 2. 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).

description of the model is given in the data section. The

satellite derived values of SIWSI for a given site is extracted

from the image as an average value of 5 pixels (center,

north, south, east and west) to reduce noise due to residual

atmospheric effects and potential geo-referencing errors.

4. Study area

The climate of Senegal is characterized by a rainy season

lasting from approximately May to October in the southern

part of the country and from July to October in the northern

part. Rainfall in the northern part of the country is very

sparse with average values of 200–400 mm/year. The

southern part of the country normally receives from 800

to 1200 mm/year. The strong North–South rainfall gradient

is a characteristic feature of the entire Sahelian region and

moving towards the north the precipitation generally dimin-

ishes with 1 mm/km as an average. Fig. 2 illustrates the

extent of the Sahelian region with Senegal located western-

most on the continent. Fig. 3 clearly illustrates the highly

variable Sahel rainfall. Since the late 1960s, the Sahelian

zone has experienced an almost 30-year period of below

1898–1993 average rainfall, which have had enormous

consequences on the rural population.

Fig. 4. Locations of the fieldwork sites in Senegal (gray stars) and the 10

rainfall stations (black dots) used for soil moisture modelling.

Table 1

In situ measured variables in 2001 and 2002

Variable name 2001 (June–November) 2002 (June–November)

In situ MODIS NDVI 10 min values 10 min values

Soil moisture (10 cm) 10 min values 10 min values

Rainfall 10 min values 10 min values

R. Fensholt, I. Sandholt / Remote Sensing of Environment 87 (2003) 111–121 115

The fieldwork has been carried out in the semiarid

northern part of the country with rainfall averages of

200–500 mm/year. The area is a marginal area in respect

to a stable presence of vegetation in general and to cultiva-

tion in particular. The inter- and intra-annual variations in

rainfall are very high and therefore the vegetation is often

exposed to water shortage during the growing season. The

vegetation of the area mainly consists of fine-leaved annual

grasses such as Schoenefeldia gracilis, Dactyloctenium

aegupticum, Aristida mutabilis and Cenchrus bifloures. Tree

and shrub canopy cover does generally not exceed 5% in the

fieldwork area and is mainly characterized by two species;

Balenites aegyptiaca and Boscia senegalensis (Diallo et al.,

1991).

5. Data

5.1. Fieldwork

Fieldwork has been performed at one location (Dahra,

Fig. 4) in 2001 and at four different locations in 2002 (two

sites near Dahra 10 km apart and two sites near Tessekre

10 km apart). From June to November both years, NDVI,

rainfall and volumetric soil moisture content in a depth of

10 cm are measured (Table 1). Soil moisture is measured

by Theta probe soil moisture sensors, type ML2x respond-

ing to changes in the apparent dielectric constant and

rainfall is measured by Rain Collector standard tipping

bucket gauges which automatically tips when a certain

amount of precipitation accumulates inside of it. Total

Fig. 5. Modelling of soil moisture Dahra 2001. Daily rainfall, measured and model

to October 20.

precipitation is determined by the number of tips. In situ

NDVI is calculated from Skye Instruments four-channel

sensors (SKR 1850 series) measuring incident and

reflected radiation in wavelengths similar to the wave-

lengths used for VI monitoring onboard the Terra MODIS

sensor (red 0.62–0.67 Am and NIR 0.84–0.87 Am). The

data has been collected using a Campbell CR10 data

logger measuring NDVI, soil moisture and rainfall every

10 min. Daily soil moisture is calculated as 24-h averages

whereas daily NDVI is calculated as the average between 9

am and 3 pm.

5.2. MODIS data processing

The satellite input data for the shortwave infrared

water stress index is the MODIS level 2G 500 m daily

surface reflectance product (MOD09GHK) years 2001

and 2002 and the MODIS level 3 surface reflectance

product (MOD09A1), which is an 8-day composite of the

gridded level 2 surface reflectance products, years 2001

and 2002. The MOD09 data product is a seven-band

product, which is an estimate of the surface spectral

reflectance for each band as it would have been measured

at ground level if there were no atmospheric scattering or

absorption (MOD09 web page, 2002). The bands are

corrected for the effect of atmospheric gases, thin cirrus

clouds and aerosols (Vermote & Vermeulen, 1999) and

the MOD09 product also includes quality control descrip-

tion of the data including information on influence from

clouds. The MODIS sensor uses a cross-track scan mirror

thereby having a large range in sensor view angles. Using

daily MODIS data therefore also introduces reflectance

variations associated with variations in the bidirectional

reflectance distribution function (BRDF). All the MODIS

data sets are originally projected onto an integerized

sinusoidal (ISIN) mapping grid. The data have been

reprojected into UTM coordinates by use of the MODIS

reprojection tool (available on the web: http://edc.usgs.

led 10 cm soil moisture and MODIS 16-day composite NDVI from June 15

Fig. 6. Modelled soil moisture versus measured soil moisture Dahra 2001

(June 15–October 20).

R. Fensholt, I. Sandholt / Remote Sensing of Environment 87 (2003) 111–121116

gov/programs/sddm/modisdist/). The resampling of data is

done by use of the bilinear interpolation resampling

algorithm.

5.3. Soil moisture modelling

Modelling volumetric soil moisture from daily rainfall

records makes it possible to test the SIWSIs from a number

of sites in the central and northern part of Senegal. Daily

rainfall data from 10 different stations in the central part of

Senegal covering the area of fieldwork was provided by the

local department of agriculture, Linguere. The locations of

the stations are given in Fig. 4. Modelling of soil moisture in

a depth of 10 cm from daily rainfall data is performed by a

simple two-layer ‘‘bucket’’ model with input parameters of

soil field capacity, wilting point (estimated from soil maps)

and vegetation density (from MODIS NDVI). The soil

moisture model distinguishes between upward water loss

from evaporation and transpiration and if the volumetric soil

water content exceeds the soil field capacity, the water will

percolate into a deeper layer. The modelling of the cumu-

lative evaporation is based on Monteith (1991) and is

Fig. 7. Modelling of soil moisture Dahra south 2002. Daily rainfall measured and

16-day composite NDVI from July 2 to October 30.

derived for partially vegetated surfaces in the Sudano–

Sahelian zone:

XEs ¼ a

ffiffit

pð3Þ

where SEs is the cumulative evaporation from the soil,

the constant a is dependent on soil hydraulic properties

and t is the time (number of days) from last rainfall

event. Soils having high clay content are, because of

efficient upward water movement due to capillarity, able

to evaporate corresponding to the potential evaporation

for 3 days before it starts decreasing with the square of

the time as given in Eq. (3). For the Sudano–Sahelian

zone in general and for the sites used in this study, soils

are however primarily sandy, falling into the luvic are-

nosol category according to FAO. Sandy soil types are

only able to correspond to the large atmospheric demand

for moisture for 1 day before evaporation starts decreas-

ing (Monteith, 1991) which means that Eq. (2) is suf-

ficient for modelling the evaporative water loss. The

transpiration loss from the soil moisture is found from

the amount of vegetation derived from the MODIS

NDVI. The MODIS NDVI 16-day composite values has

been linearly interpolated to daily values and the tran-

spiration loss was estimated empirically as a residual by

fitting the modelled soil moisture curves to the mea-

sured by adding weights to the transpiration term in the

calculation.

The model is calibrated from the fieldwork sites 2001

(one site) and 2002 (four sites) where both 10 cm soil

moisture and rainfall are known. Results from two sites are

presented here but all five sites are relatively similar in

respect to the model agreement with measured soil moisture.

Figs. 5 and 7 show the time series of rainfall, soil moisture

(measured and modelled) and NDVI (satellite and in situ

measurements). The model agreement with measured soil

moisture is given in Figs. 6 and 8.

modelled 10 cm soil moisture, in situ measured MODIS NDVI and MODIS

R. Fensholt, I. Sandholt / Remote Sensing of Environment 87 (2003) 111–121 117

The overall agreement between model values and mea-

sured values is good allowing the modelling of daily soil

moisture from daily rainfall records. For both years, the

largest scatter occurs when the soil moisture is at field

capacity. This is because soil moisture is a 24-h average

value while the rainfall is a single value, meaning that, e.g.

if the rainfall event is in the evening the major part of the

soil moisture change will be attributed to the following day.

Another uncertainty is that the soil moisture model uses the

MODIS 16-day NDVI values which does not always

precisely reflect the short time vegetation variations. The

in situ measured MODIS NDVI in Fig. 7 reveals a large

decrease not captured by the MODIS NDVI around August

23, resulting in an overestimation of the transpiration in that

period leading to an underestimation of the volumetric soil

moisture.

6. Results and discussion

6.1. Comparing SIWSI with in situ soil moisture

measurements

Canopy water stress measured from satellite (the SIWSI

(6,2) configuration) is tested against in situ measurements of

soil moisture in Fig. 9. The 2001 rainfall in the central and

northern part of Senegal in general reflects the average

values of 300–500 mm. For the area of fieldwork, 2001

was slightly drier with an annual total of 288 mm. Volu-

metric soil moisture content measured in 10 cm is reflecting

the water availability for the plant photosynthetic activity

and a decrease in soil moisture are expected to cause an

increase in the SIWSI(6,2).

Vegetation growth begins around August 1 and in the

beginning of the growing season there is no relation between

the SIWSI(6,2) andmeasured soil moisture which is expected

because of the low initial vegetation cover. As the vegetation

is progressively getting denser an inverse relation between

SIWSI(6,2) and soil moisture emerges. From the figure, this

Fig. 8. Modelled soil moisture versus measured soil moisture Dahra south

2002 (July 2–October 30).

relation can be found approximately from August 20 and

throughout the entire growing season. The pattern is under-

lined by drawing lines through the daily SIWSI(6,2) values

for all major rainfall events causing a significant chance in

soil moisture availability. One exception is however found on

the 25 of September where a small rainfall event does not

significantly change the soil moisture content in the depth of

10 cm, but have an impact on the SIWSI(6,2) the following

days. This can be explained by the distribution of the plant

roots, which are mainly located in the upper 10 cm of the soil.

After the last rainfall events, 2–5 October, the vegetation

enters the stage of senescence. This is also reflected in the

SIWSI(6,2) but the fact that the vegetation begins to wither at

that time is probably not because of water shortage but

because the annual grasses are photoperiodic (Ridder et al.,

1983). Photoperiodism means that the flowering date is

controlled by the length of the day regardless if the germi-

nation date varies, meaning, e.g. that a late rainy season will

not just postpone the growing season but make it shorter.

Once the phase of senescence has started in October, rainfall

thus has no influence on the viability of the grasses, which

can be noticed in Fig. 7 where 40 mm of rain from 9 to 13

October has no influence on the succeeding NDVI values.

The SIWSI(6,2) and (5,2) are plotted against soil mois-

ture in Fig. 10 from August 15 to October 29. A clear

exponential relationship is present for (6,2) with an r2 =

0.87. It appears that the scatter around the regression line

is higher for low values of SIWSI(6,2), meaning unstressed

conditions. This can be explained by an upper limit of leaf

water content corresponding to an SIWSI(6,2) value of

approximately � 0.2 from where the canopy water content

is no longer responding to an increase in soil water

content. The relation between SIWSI(5,2) and soil mois-

ture in Fig. 10 is also high (r2 = 0.79). The scatter around

the regression line is of the same magnitude as (6,2) but

the explanation for the lower r2 value is the dynamic range

of the SIWSI(5,2) which is smaller than the dynamic range

of the SIWSI(6,2).

In situ measurements of MODIS SIWSI(6,2) is also

plotted against the NDVI in Fig. 10. The r2 value of 0.81

illustrates that a large amount of the information contained

in SIWSI(6,2) is also present in the NDVI expression. The

correlation is however not as good as it is the case for the

SIWSI(6,2)/soil moisture relation. The small fluctuations

illustrated by the lines through the daily SIWSI(6,2) values

in Fig. 9 are not captured by the NDVI signal underlining

that the SIWSI(6,2) is not just another way of expressing

vegetation intensity but add additional information about the

canopy water availability. This point is further illustrated in

Fig. 11, which is similar to Fig. 10 but for a shorter time

period (from August 15 to October 8).

Fig. 11 illustrates the relation between MODIS SIWSI

(6,2), respectively, in situ measured simulated MODIS

NDVI versus in situ measured soil moisture for a period

representing the vegetative phase not including the phase

of senescence. This period of time corresponds to the

Fig. 9. Daily measurements of rainfall (annual sum= 288 mm) and soil moisture, MODIS SIWSI(6,2) and MODIS 16-day composite NDVI in Dahra from June

15 to October 29, 2001.

R. Fensholt, I. Sandholt / Remote Sensing of Environment 87 (2003) 111–121118

phenological stage where available soil water influences

the plant growth, e.g. the period where it would be de-

sirable to add information on canopy water stress when

modelling NPP. When only the vegetative phase of the

grasses is considered and the overall phenological vegeta-

tion cycle is not included it is clear from Fig. 11 that

information on soil moisture, and thereby canopy water

stress, is still reflected by the SIWSI(6,2) indicated by the

r2 value of 0.48, whereas the redundancy between SIWSI

(6,2) and NDVI is small with an r2 value of 0.27. Finally,

Fig. 12 demonstrate a poorer relation between NDVI/soil

moisture than SIWSI(6,2)/soil moisture (Fig. 11) support-

ing the stated hypothesis.

In situ measurements from the four sites covered in

2002 do not consolidate the findings of 2001. The year

2002 was an extremely dry year with annual total rainfalls

of 150–200 mm in the fieldwork area, which is below

50% of the normal annual rainfall. Furthermore, the

Fig. 10. SIWSI(6,2) and SIWSI(5,2) plotted against soil moisture and

SIWSI(6,2) plotted against NDVI from August 15 to October 29, Dahra

2001.

seasonal distribution of rain was uneven with long dry

spells resulting in a very short growing season character-

ized by an extremely low vegetation cover. The MODIS

NDVI values 2002 do not reach 0.4 for two successive

composite periods for any of the four sites, which are

considerable lower then vegetation cover 2001 where

MODIS NDVI exceeds 0.4 from the end of July to the

middle of October (Fig. 9). It seems likely that the

vegetation cover in 2002 simply is too sparse to be able

to detect variations in canopy water content from the

SIWSIs. Further work is needed to determine the lower

NDVI threshold value at which the stress index works

using the combination of the NIR and SWIR wavelengths.

For low vegetation cover fraction, the potential influence

from soil colour variations on the near infrared band also

needs to be studied. Finally, the reflectance variations

associated with variations in the BRDF would also need

to be addressed.

Fig. 11. SIWSI(6,2) plotted against soil moisture and SIWSI(6,2) plotted

against NDVI from August 15 to October 8, Dahra 2001.

Fig. 12. NDVI plotted against soil moisture from August 15 to October 8,

Dahra 2001.

Fig. 14. The r2 values of the correlation between SIWSI(6,2) and

SIWSI(5,2) versus modelled soil moisture for 10 different rainfall stations.

R. Fensholt, I. Sandholt / Remote Sensing of Environment 87 (2003) 111–121 119

6.2. Comparing SIWSI with modelled soil moisture

To be able to compare the satellite driven stress index to

soil moisture for a number of different sites in 2001 the

SIWSIs are compared to modelled soil moisture for 10

rainfall stations in the central and northern part of Senegal

(Fig. 4). The results of the SIWSI(6,2)/soil moisture and

SIWSI(5,2)/soil moisture scatterplot for a single rainfall

station representing the median r2 value of all stations are

shown in Fig. 13.

The results generally reflect the same relation as the

SIWSI/in situ measured soil moisture in Fig. 10. The

explanation of variance is somewhat lower than the

SIWSI/in situ measured soil moisture relations which can

be expected from modelled soil moisture, but the relation

between the SIWSIs and modelled soil moisture is evident.

Whereas the SIWSI(6,2) correlated better to in situ mea-

sured soil moisture than SIWSI(5,2) this is not the case for

the 10 rainfall stations. The r2 values of all rainfall stations

are summarized in Fig. 14. The results show that the two

Fig. 13. The SIWSI(6,2) and SIWSI(5,2) plotted against modelled soil

methods of estimating canopy water stress perform quite

identical and suggest that combined information from the

near- and shortwave infrared wavelengths can be useful as

an indicator of canopy water stress.

In comparison to other methods like the Ts/NDVI space

approach, there are both advantages and disadvantages of

the SIWSI. The Ts/NDVI space approach has been tested in

a number of studies and it is well known that Ts is very

sensitive to residual effects from clouds and cloud shadows,

atmospheric influence from aerosols and water vapour and

finally to variations in surface emissivity (Wan, 1999). For

the Sahelian area the practical use of the Ts/NDVI space

approach therefore is seriously constrained in the rainy

season because the number of MODIS Ts data defined as

good quality is very limited. When the purpose of a plant

water stress index is to form part of NPP modelling this

clearly is undesirable because all of the vegetation growth

takes place in the rainy season. On the other hand, it is

suggested that the Ts/NDVI space approach does apply to

very sparse vegetation cover (less than MODIS NDVI = 0.4)

where the canopy signal in terms of SIWSI appears to be too

weak. It is therefore likely that the SIWSI can be a valuable

supplement to the use of the Ts/NDVI space approach when

moisture for the Dahra Meteo rainfall station in Senegal, 2001.

Fig. 15. Same as in Fig. 14 but with 8-day SIWSI(6,2) versus 8-day

(average values) modelled soil moisture appended.

R. Fensholt, I. Sandholt / Remote Sensing of Environment 87 (2003) 111–121120

deriving information on canopy water stress and the com-

bined use of these complementary data sources must be

studied further.

It has been investigated whether the use of 8-day

composite MODIS data versus 8-day average soil moisture

values compared to the daily data, will provide more robust

correlations. This hypothesis has been tested for the 10

rainfall stations and the results are illustrated in Fig. 15. It is

clear that the 8-day values correlate better than the daily

data. Especially the r2 values of rainfall stations 7 and 8,

which had the lowest coefficients of explanation using daily

data, have improved considerably. It is however question-

able if these r2 value improvements are useful in modelling

the inter-seasonal canopy water stress. It is likely that the

variations depicted by the 8-day composite values of SIWSI

are cognate to the NDVI signal. As it was shown in Fig. 10,

there is a considerable redundancy between the SIWSI and

NDVI and it is likely that 8-day composite SIWSI values do

not capture the small fluctuations illustrated by the lines

through the daily SIWSI(6,2) values in Fig. 9, but merely

reflects an overall relation between vegetation intensity and

water availability.

7. Conclusions and perspectives

A shortwave infrared water stress index derived from

MODIS near- and shortwave infrared channels has been

evaluated against in situ measurements of soil moisture,

rainfall and NDVI in the semiarid Sahelian zone in West

Africa. Daily values of two different configurations of the

SIWSI denoted SIWSI(6,2) and SIWSI(5,2) have been

compared to soil moisture measured in the 10 cm. The year

2001 rainfall in the region was slightly below average and

the results show a strong correlation between SIWSI and

soil moisture. The SIWSI(6,2) performs marginally better

than the SIWSI(5,2) (r2 = 0.87 and 0.79) when looking at

the vegetative and reproductive phase. Restricting the anal-

ysis to the vegetative phase only, which is the one of interest

using canopy water stress for modelling of NPP, it can be

concluded that the SIWSI reflects the short time soil

moisture variations better than the NDVI. The SIWSI/soil

moisture gives r2 = 0.48 and the NDVI/soil moisture gives

r2 = 0.29 and furthermore the redundancy between the

SIWSI and NDVI is low (r2 = 0.27).

To test the robustness of the SIWSI, soil moisture has

been modelled from daily rainfall data at 10 sites in the

central and northern part of Senegal. The correlations

between SIWSI and simulated soil moisture are generally

high with a median r2 = 0.72 for both configurations of the

SIWSI. The results look promising and in contrast to the

comparison using in situ measured soil moisture, the two

methods of estimating canopy water stress perform quite

identical. It is therefore suggested that the combined

information from the MODIS near- and shortwave infrared

wavelengths can be useful as an indicator of canopy water

stress in the semiarid Sahelian environment. In this paper,

the point of departure was the development of a canopy

water stress index to form part of NPP modelling in Sahel,

where all of the vegetation growth occurs in the rainy

season characterized by high atmospheric water vapour

content and an extensive cloud cover. Particularly in the

rainy season the SIWSI therefore can be a valuable

supplement to the use of the Ts/NDVI space approach

which is known to be very sensitive to clouds and atmos-

pheric effects minimizing the availability of Ts data. The

combined use of these complementary data sources must

be studied further.

The fieldwork in 2002 did not verify the results found in

2001. However, year 2002 was an extremely dry year and

the vegetation cover apparently was too sparse to provide

information on the canopy water content. The SIWSI

therefore must be tested against more time series of in situ

data for different vegetation cover fractions and the influ-

ence from the soil background also needs to be studied

further. The possibilities of purchasing multispectral radio-

meters matching the MODIS channels 5 and 6 for fieldwork

is currently investigated.

Acknowledgements

The study is funded by the Danish Research Councils,

ESA related research, Grant No. 9902490. Scientific

equipment for fieldwork is funded by the Danish Research

Agency, Danish Agricultural and Veterinary Research

Council. The authors would like to thank the MODIS

Land Discipline Group for creating and sharing the

MODIS LAND data. The Centre de Suivi Ecologique in

Dakar is kindly acknowledged for providing logistic

support. Thanks also to the staff at Institut Senegalais de

Recherhes Agronomiques (ISRA) in Dahra for support

during the fieldwork. We are grateful to Ulf Pierre Thomas

for the preparation and the design of the fieldwork

equipment. Jørn Torp Pedersen, Mette Wolstrup and

Anette Nørgaard are kindly thanked for their fieldwork

assistance. The comments of the anonymous reviewers

greatly improved the manuscript.

R. Fensholt, I. Sandholt / Remote Sensing of Environment 87 (2003) 111–121 121

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