REGULAR PAPER
Predicting vegetation water content in wheat using normalized difference water indices derived from ground measurements
Chaoyang Wu ÆZheng Niu ÆQuan Tang ÆWenjiang Huang
大头娃娃舞
Received:24July 2008/Accepted:21December 2008/Published online:26February 2009ÓThe Botanical Society of Japan and Springer 2009
Abstract Vegetation water content (VWC)is an important variable for both agriculture and forest fire management.Remote nsing technology offers an instantaneous and non-destructive method for VWC asssment provided we can relate in situ measurements of VWC to spectral reflectance in a reliable way.In this paper,bad on radiative transfer models,three new normalized difference water indices (NDWI)are propod for VWC [fuel moisture content (FMC),and equivalent water thickness (EWT)]estimation,taking both leaf internal structure and dry matter content into account.Reflectance at 1,200,1,450and 1,940nm were lected and normalized with reflectance at 860nm to establish three water indices,NDWI 1200,NDWI 1450and NDWI 1940.Good correlations were obrved between FMC (R 2=0.65–0.80)and EWT (both at the leaf scale,R 2=0.75–0.81for EWT L and at the canopy scale,R 2
=0.80–0.83for EWT C )at various stages of wheat crop development.
Keywords Equivalent water thickness ÁNormalized difference water index ÁVegetation water content
史进绰号
Introduction
Remote nsing has provided a potential means to over-come the limitations of traditional methods of large-scale vegetation sampling by offering a non-destructive and timely approach at the landscape scale (Chen et al.2005;Davidson et al.2006).The fundamental basis of monitoring vegetation water content (VWC)through remote nsing is that reflectance in the near-infrared (NIR,700–1,300nm)and short-wave infrared (SWIR,1,300–2,500nm)will change with variations in water status (Ceccato et al.2001;Danson and Bowyer 2004;Seelig et al.2008).In order to reduce the influences of leaf structural,background soil contamination and of atmospheric effects on a single band reflectance,vegetation indices (VI)compod of two or more bands are derived (Gao 1996;Zarco-Tejada et al.2003).
Recently,rearchers have explored methodology for VWC estimation through remote nsing techniques bad on radiative transfer models such as PROSPECT (Jacque-moud and Baret 1990)and SAILH (Kuusk 1985).PROSPECT is a leaf-scale model that can simulate reflectance and t
ransmittance from 400to 2,500nm by four foliar biochemistry and scattering parameters (leaf struc-ture parameter,N ;chlorophyll content,C a ?b ;equivalent water thickness,EWT;and dry matter content,Dm).SAILH (scattering by arbitrary inclined leaves)is a canopy model bad on a four-stream approximation of the radia-tive transfer equation with two direct fluxes (incident solar flux and radiance in the viewing direction)and two diffu fluxes (upward and downward hemispherical flux).
More typically,VWC can be estimated by simple ratio of reflectance values of two bands,including a reference band where the water absorption coefficient is low and a measurement band where water absorption is moderate or
C.Wu (&)ÁZ.Niu ÁQ.Tang ÁW.Huang
The State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing Applications,
Chine Academy of Sciences,100101Beijing,China e-mail:
C.Wu ÁQ.Tang
Graduate University of Chine Academy of Sciences,100039Beijing,China
W.Huang
National Engineering Rearch Center for Information Technology in Agriculture,100089Beijing,China
J Plant Res (2009)122:317–326DOI 10.1007/s10265-009-0215-y
high(Gao1996).As most water indices are propod bad on the ratio of two bands,with both water absorption and other features N and Dm),the lection of wavelengths is critical for the nsitivity of the index to changes in plant water status(Eitel et al.2006).The influence of VWC is en mainly in the NIR and SWIR (Gao1996;Pen˜uelas et al.1997).Thus,incorporating reflectance in NIR–SWIR will provide a pigment-inde-pendent quantitative estimate of VWC,although this method still needs further refinement to account for the obrved effects of leaf structure,leaf dry matter,canopy structure and leaf area index(LAI)(Zarco-Tejada et al. 2003).
In the SWIR region,specific water absorption features may provide insight for VWC ,wavelengths 970,1,200,1,600,1,950and2,250nm(Bowyer and Danson2004;Claudio et al.2006).However,it has also been demonstrated that remote nsing using a single SWIR wavelength range alone is not sufficient in the estimation of VWC as two other leaf parameters(N and Dm)are also responsible for leaf reflectance variations in the SWIR region(Ceccato et al.2002).I
n order tofind the most appropriate bands for VWC estimation,Ceccato et al. (2002)performed a leaf-level nsitivity analysis of the PROSPECT model(Jacquemoud and Baret1990)in which it was demonstrated that1,457and860nm were the wavelengths with the highest and lowest nsitivity to leaf water variation,respectively.Reflectance of860nm is deemed an effective reference band for the estimation of VWC due to its relatively better canopy penetration ability. This conclusion was further confirmed by later studies (Eitel et al.2006;Colombo et al.2008;Cheng et al.2008). However,the lection of a measurement band where water absorption is high is much more complicated.First,the band must be well detectable if there is a variation in VWC becau reflectance in some bands cannot be detected under slight or moderate water stress(Eitel et al.2006). This means that bands with strong absorption features are the most appropriate candidates for the measurement band (Jackson et al.2004;Eitel et al.2006;Colombo et al. 2008).Second,the measurement band must have a rela-tively high incoming energy and low level of interference from atmospheric water vapor,indicating that the longer wavelength in the SWIR region may not be suitable for VWC estimation(Sims and Gammon2003;Claudio et al. 2006).
Despite arguments over band lection,different vege-tation water indices[normalized difference water index (NDWI),normalized difference infrared index(NDII), maximum difference water index(MD
WI),water band index(WBI),R1300/R1450and T1300/T1450]compod of bands in the absorption peaks have proved to be appli-cable in the estimation of VWC(Chen et al.2005;Stimson et al.2005;Claudio et al.2006;Eitel et al.2006;Yilmaz et al.2008;Yebra et al.2008;Seelig et al.2008).
Traditionally,there are two ways of asssing the VWC of a leaf.Thefirst definition is fuel moisture content (FMC),defined as the ratio between the quantity of water [fresh weight(FW)-dry weight(DW)]and either FW or DW(Chuvieco et al.1999):
FMC¼FWÀDW
ðÞ=FWðor DWÞÂ100%ð1Þwhere FW is the fresh weight measured in thefield,and DW is the oven dry weight of the same sample.FMC express the amount of water in a leaf relative to the amount of fresh weight or dry matter and is related to both leaf water content and leaf dry matter content.
The other definition,which is more commonly ud in the remote nsing literature,is leaf water content per unit leaf area,or EWT(in units of g cm-2).EWT relates to a hypothetical thickness of a single layer of water averaged over the whole leaf area(Danson et al.1992)
EWT L¼ðFWÀDWÞ=Að2Þwhere A is the one-side leaf area and EWT L reprents EWT at leaf scale.Equation2requires an independent measurement of leaf area.
At a canopy scale,EWT C(in units of g m-2)is deter-mined as the product of EWT L and green LAI(Colombo et al.2008):
EWT C¼EWT LÂLAIð3ÞThe objective of this paper is to prent a study of NDWI in VWC(both FMC and EWT)estimation by incorporating more bands that are responsive to water variation signals.For clarity,we have listed all abbreviations and term ud in this study in Table1, including some notes and descriptions.Wefirst introduced some bands in the NIR and SWIR regions for EWT evaluation bad on the PROSPECT model.New NDWIs were propod by taking account of leaf internal structure and dry matter effects.A validation study in wheat was carried out on four days(17April,28April,16May and29 May)in2007,corresponding to typical crop growth phas of wheat.
Materials and methods
Study sites and materials
The study area is located at the National Experiment Station for Precision Agriculture(40°10.60N,116°26.30E)20km northeast of Beijing,China.This experimental station has been operational since2001and is ud for precision agri-culture rearch.The site is located within
a warm-
temperate zone with a mean annual rainfall of507.7mm and a mean annual temperature of13°C.The plant lected for this study was winter wheat(Triticum aestivum L.),which is one of the most important crops in China.
Ground measurements
Canopy reflectance acquisition
Six cultivars of winter wheat that can be classified into three leaf structural types were ud in this experiment (Table2).Each winter wheat cultivar was cultivated in an area of about4,000m2(about200m920m).Wheat grew in a silt clay soil with sufficient water supply.Data was collected in the morning local time on four clear days during a typical wheat growth ason:17 April,28April,16May and29May in2007,corre-sponding to jointing,heading,flowering and ripening phas,respectively.
Canopy radiance data were collected from380nm to 2,500nm(1nm sampling interval)using a portable spectroradiometer(FS-FR2500,Analytical Spectral Devi-ces,Boulder,CO)withfield of view of
25°normal to the canopy located at a distance of approximately100cm from the canopy surface.Reflectance spectra were derived through calibration relative to a99%white reference panel (Labsphere,North Sutton,NH).Measurements were con-ducted systematically at each plot.
Leaf and canopy EWT calculation
刘邦是哪个朝代Twenty individual leaves of wheat were randomly col-lected in thefield and immediately aled in plastic bags and placed on ice until analysis.In the laboratory,the leaf samples were cut with a knife into pieces approximately 3cm long.Fresh weight was obtained using an analytical balance and then samples were dried at80°C in an oven for24h to measure the DW values.The leaf areas of all samples were measured with a portable area meter LI-3000A(LI-COR,Lincoln,NE).
Leaf area index calculation
All aboveground plant materials within a0.6m90.6m area were collected immediately following spectral mea-surements.Leaves of all the sampled plants were collected to determine the LAI.A subsample of plant leaves was ud to measure the leaf area in the laboratory with the Li-COR 3100A area meter.The leaf area of the subsample(LAI sub) was ud to calculate the LAI of the0.6m90.6m sample area.
Sensitivity study
Leaf reflectance simulation using the PROSPECT model The PROSPECT model was adopted to analyze leaf reflectance changes due to variations in water content.To examine the effect of water content on reflectance spectra, other parameters were assignedfixed values(thefixed values are determined through in situ measurements)and the EWT was changed from0.002to0.03g cm-2in steps of0.002(Table3).The lection of parameter values was bad on Hosgood et al.(1995).For the simulation, reflectance was acquired from600to2,400nm at10-nm intervals.
Table1Abbreviations ud in this paper
Abbreviation Definition(units)
Water content descriptions
VWC Vegetation water content(%)
CWC Canopy water content(g m-2)
EWT L Leaf equivalent water thickness(g cm-2or cm) EWT C Canopy equivalent water thickness(g m-2) Indices
VI Vegetation indices
WI Water indices
NDVI Normalized difference vegetation indices NDWI Normalized difference water indices
NDII Normalized difference infrared index
头发英语怎么读MDWI Maximum difference water index
WBI Water band index
Model input parameters
N Leaf internal structure
Dm Dry matter content(g cm-2)
Cw Water content(g cm-2or cm)
Cab Chlorophyll content(l g cm-2)
LAI Leaf area index(m2m-2)
Others
FW Fresh weight(g)
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DW Dry weight(g)
SWD Specific water density(g cm-2)
SLW Specific leaf weight(g cm-2)Table2Description of wheat cultivar
Number Cultivar Canopy leaf orientation Leaf colour
1Laizhou3729Erectophile Dark green 2Chaoyou66Spherical Dark green 3Linkang2Planophile Dark green 4Jing8Spherical Dark green 5Jing411Erectophile Light green 69507Planophile Light green
Canopy reflectance simulation with SAIL model
Canopy reflectance spectra were simulated using the SAIL model(Verhoef1984),as revid for taking hotspot effect into consideration(Kuusk1985).We ud the models to simulate canopy reflectance tcl移动空调
and investigate the variation of indices due to changes in LAI.We considered the effect of changing LAI from0.5to7in steps of0.1while other parameters were t to pre-determined values(Table4).To avoid uncertainty in running the model in the ca of Chine wheat,all parameters was evaluated by deter-mining empirical values and soil reflectance was determined using in situ measurements.
Results
Leaf water absorption features
Becau different cultivars have different canopy leaf ori-entations,the spectra differed depending on the cultivar. The in situ canopy reflectance of the samples is shown in Fig.1.In the simulation part,different EWT exerted an evident effect on reflectance from900to2,400nm,while little effect was en in the red edge region(e Fig.2).In this region,there were typically four absorption peaks, centered on970,1,200,1,450and1,940nm.Reflectance of the bands showed strong nsitivity to leaf EWT values. The absorption features became much deeper as EWT incread from0.002to0.03g cm-2,indicating their potential for water content monitoring.
Of the main water absorption features(970,1,200,1,450 and1,950nm),reflectance at970nm was not lected as there was no relationship between leaf reflectance and specific water density(SWD)at97
5nm(Danson et al. 1992).Wavelengths longer than2,000nm were also excluded due to relatively low incoming energy and high levels of interference from atmospheric water vapor(Sims and Gammon2003).
To e the absorption features clearly,band reflectance was plotted as a function of EWT.Figure3shows that reflectance at860nm was totally independent of EWT variation,which makes it a suitable candidate for a refer-ence band.For the other three strong absorption bands, reflectance at1,200nm exhibited a linear relationship with EWT,while the other two showed certain saturation regions,especially for reflectance at1,940nm,which became saturated quickly after an EWT of0.01g cm-2, indicating that reflectance at1,450and1,940nm are very nsitive to low values of EWT and will lo its nsitivity at high EWT values.
Derivation of normalized difference water indices Bad on combinations of bands where water absorption is high and low,three new NDWIs are propod,
NDWI1200¼ðR860ÀR1200Þ=ðR860þR1200Þ
NDWI1450¼ðR860ÀR1450Þ=ðR860þR1450Þ
NDWI1940¼ðR860ÀR1940Þ=ðR860þR1940Þ
ð4Þwhere R x reprents reflectance at x nm.
Table3Parameters ud in simulating leaf reflectance using the PROSPECT model
Parameter Values(unit)Notes
N 1.35Leaf structure parameter Cw0.002–0.030g cm-2Equivalent water thickness Dm0.01g cm-2Dry matter content
C a?b35l g cm-2Chlorophyll a?b content Table4Parameters ud in simulating canopy reflectance using the SAILH(scattering by arbitrary inclined leaves)model
SAILH parameters Values
Leaf optical properties a N=1.55;Cab=35l g cm-2,
Cw=0.01g cm-2,
Dm=0.01g cm-2
Soil reflectance In situ average measurements at
ten spots
香港大屿山LAI0.5–7in0.1steps
Leaf angle distribution Spherical
Sun zenith angle45°
Sensor view angle0°(nadir)
Fraction specularflux1
a Leaf reflectance and transmittance were simulated with PROSPECT with the input parameters shown(e Table1for
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definitions)Fig.1Spectral curves of the sample wheat from400nm to2,400nm from data obtained on17April2007.Band regions1,360–1,380and 1,780–1,930nm were excluded becau of decread nsitivity of the ASD spectroradiometer(Analytical Spectral Devices,Boulder, CO)in the regions
The three NDWIs were plotted as a function of EWT L with data simulated from the PROSPECT model (Fig.4).To compare the indices directly,we scaled the nsitivity results from 0to 1.Similar to the characteristics of the single band,NDWI 1200displayed an almost linear rela-tionship with increasing EWT.But NDWI 1940showed a rapid ri at low EWT values (below 0.008g cm -2)and quickly became saturated after reaching a value of 0.016g cm -2.NDWI 1450fell between the two.Figure 4implies that NDWI 1940is not a suitable index for high EWT leaves but shows a high nsitivity to low EWT,as evident from the larger variation in NDWI 1940in the low EWT range.On the contrary,the nearly linear relationship between NDWI 1200and EWT tentatively suggests that NDWI 1200is suitable for interpreting high EWT levels.
Effect of leaf internal structure and dry matter content on NDWIs with simulated leaf reflectance
The effects of leaf internal structure (N )and dry matter content (Dm)were studied using the PROSPECT model by fixing other parameters at constant values.For reflectance simulation with N v
ariations (N =1,1.5,2,2.5),chloro-phyll content (Cab),Dm and water content (Cw)were 0.035,0.01and 0.01g cm -2,respectively.For the ca of Dm variation,the parameters N ,Cab and Cw were 1.5,0.035and 0.01g cm -2,respectively.
Figure 5depicts the relationships between the NDWIs and N (Fig.5a)and dry matter content (Fig.5b).For all NDWIs,variations in leaf structure N and Dm exerted strong
effects.
Fig.3Reflectance variations at 860,1,200,1,450and 1,940nm with equivalent water thickness (EWT )changes from 0.002to 0.03g cm -2.Note saturation of reflectance at 1,940nm with EWT
variations
Fig.4Relationship between different normalized difference water indices (NDWIs )and EWT variations at leaf level.Note the rapid ri in NDWI 1940to a value \0.008g cm -2and almost complete saturation by 0.016g cm -
2
Fig.2Water absorption features of leaf spectra (600–2,400nm)simulated with the PROSPECT model
