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An ASAE/CSAE Meeting Presentation Paper Number: 043065

Estimating Water Stress in Plants UsingHyperspectral Sensing

Carol L. Jones, Research Engineer

Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK

Paul R. Weckler, Assistant Professor

Biosystems and Agricultural Engineering, Oklahoma State University

Niels O. Maness, Professor

Horticulture and Landscape Architecture, Oklahoma State University

Marvin L. Stone, Regents Professor


Roshani Jayasekara, Research Engineer


Written for presentation at the2004 ASAE/CSAE Annual International Meeting

Sponsored by ASAE/CSAEFairmont Chateau Laurier, The Westin, Government Centre

Ottawa, Ontario, Canada1 - 4 August 2004

Abstract. The ability to estimate plant water content may provide valuable information to environmental and irrigationsystem managers to relieve dehydration symptoms and prevent permanent growth and production damage. A

portable spectroradiometer was used to gather hyperspectral reflectance data from three plant species (corn, spinachand snap beans) grown in a greenhouse and subjected to different watering conditions to instigate different levels ofmoisture deficiency. Spectral bands at 950-970, 1150-1260, 1450, 1950, and 2250 nm and five indices (WI, NDVI,SIPI, fWBI, and WI/NDVI) were analyzed to determine the best method of nondestructively estimating plant water

content. In corn and snap beans, the 1450 nm band data provided the best estimate (r2= 0.67 and 0.50). In spinach,reflectance data from the 950-970 nm bands were the most useful (r

2= 0.94).

Keywords. Reflectance, hyperspectral, moisture content, water stress, spectroradiometer, spectral indices, plants,crops


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Introduction

An accurate estimate of plant water content is significant in making management decisions forirrigation, wildfire risk, and drought assessment (Chandler, et al., 1983; Penuelas, et al., 1993;Penuelas, et al., 1996). A method to nondestructively assess in real time plant water content

(PWC) in plants may eliminate the tedium and delay associated with oven-drying (Allen, 1989).Variations in reflectance related to OH bonding of water may provide a method ofnondestructively estimating plant water content in certain plant species (Palmer and Williams,1974). Absorption of infrared radiation has been tested as a method of estimating plant waterconcentration (Carter, 1991; Danson, et al., 1992; Jackson, 1984; Hatfield and Pinter Jr., 1993)

Researchers have investigated different spectral bands and for water sensitivity. The spectralbands at 950-970, 1150-1260, 1450, 1950, and 2250 nm have shown promise in estimatingwater content in certain species (Sims and Gamon, 2003).

Water content sensitive spectral indices are typically combinations between reflectance orintensity at wavelengths where water absorbs energy at different magnitudes. Spectral indicesthat have been developed using these water bands (water band indices) are: relative water

content (Penuelas, et al., 1993; Penuelas, et al., 1996), leaf water potential (Penuelas, et al.,1993; Penuelas, et al., 1996; Pierce, et al., 1990; Riggs and Running, 1991), water index (WI)(Penuelas, et al., 1997), normalized difference vegetation index (NDVI) (Penuelas, et al., 1997;Rouse, et al., 1974), water index divided by normalized difference vegetation index(WI/NDVI)(Penuelas, et al., 1997), structure-independent pigment index (SIPI) (Penuelas, et al.,1995), and floating-position water band index (fWBI) (Strachan, et al., 2002). Each of theseindices has been successful with different plant species and is sensitive to the structure of theplants and the method in which the data were collected (Sims and Gamon, 2003).

The wavelength regions considered in this research were 950-970, 1150-1260, 1450, 1950, and2250 nm and the indices investigated included WI, NDVI, SIPI, fWBI, (Table 1) and WInormalized with NDVI (WI/NDVI).

Table 1. Summary of investigated indices. Rxxx = reflectance. Subscript wavelengths representa narrow range of 10 nm.

Index Equation Reference and Notes

Water Index (WI)

970

900

R

RWI =

(Penuelas, et al., 1997; Sims and Gamon,2003)-significantly correlated with plant water contentwhen a wide range of water content isconsidered.

Normalized differencevegetation index (NDVI)

( )( )640800

640800

RR

RRNDVI

+

= (Rouse, et al., 1974; Sims and Gamon, 2003)-found less sensitive to water content than otherwater band indices

Structure-independent

pigment index (SIPI)

( )

( )680800445800

RR

RR

SIPI

=

(Penuelas, et al., 1995)

-uses blue and red wavelengths to assessproportion of total photosynthetic pigments tochlorophyll.

Floating-position waterband index (fWBI)

)Rmin(R

RfWBI

980930

900

= (Strachan, et al., 2002)-position of spectral water absorption troughshown to be dynamic during stress conditions,reflected in the range in the denominator


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The objectives of the research reported here are to:

Assess the accuracy of hyperspectral reflectance information in estimating plant watercontent in corn, spinach and snap beans. These crops were chosen because of theirinherent differences in foliage presentation.

Identify the reflectance index that provides the best precision in providing an estimate of

plant water content in each species.

Methods and materials

Plant Sampling

Corn, spinach and snap beans were grown in a greenhouse in standard flats. One flat of eachspecies was watered according to best practices and used as a control. Remaining flats weresubjected to desiccation culminating in a permanent wilt stage. Beginning at the post cotyledonstage in spinach and snap beans and the V2 stage in corn, plants were chosen randomly everyother day for sampling. At least ten hyperspectral measurements from different locationsthroughout the foliage of each chosen plant were recorded using a spectroradiometer with a

self-contained illumination system. The readings were taken directly at the leaf surface withcare taken to exclude any external light introduction. The vegetative portions of the plants weresubsequently harvested and moisture content was determined using oven methods. Weightswere recorded before the plants were placed in the oven (70C) and at scheduled intervals untilno change in weight was observed. Weight change was assumed to be due to loss of watercontent. PWC was determined using the weights obtained from the oven testing method andcomputed using the following equation:

PWC (%) = ((WF WD)/WD)100 (1)

where: WF = wet weight, g

WD = dry weight, g

Spectroradiometer

Hyperspectral data were collected using a Field Spec Pro JR portable spectroradiometer(Analytical Devices, Boulder, Colorado) connected serially to a laptop computer. Thisspectrometer was equipped with a high intensity contact probe that housed a halogen bulb andprovided a 10 mm spot. The spectroradiometer was calibrated before and after readings foreach plant using a barium sulfate coated reflectance plate (white plate). Both dark and whitereadings were used for calibration according to the manufacturers directions. The Field SpecPro JR gathers irradiance, computes reflectance by subtracting dark current and dividing by thewhite plate reading and records the reflectance in the 300 to 2400 nm wave bands. UsingViewSpec Pro, version 3.06 software (Analytical Devices, Boulder, Colorado) to assemble and

interpret data, files were converted and imported into statistical software for further analysis.

Experiment Structure

This study was conducted as a split plot design with the plant species as the main plots and thecollection days as the subplots. Samples were collected on seven different days for each plantspecies. Within the subplots, two plants were randomly removed each day from growing flatsthat were watered according to best practices for maximum production and two plants wererandomly removed from flats that had received no water subsequent to the beginning of the


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study at post-cotyledon stage for spinach and snap beans and V2 for corn. Hyperspectral datawere collected from ten random locations in the vegetative portion of each plant.

Data Analysis

Spreadsheet software was used to organize data. Reflectance data at 435 - 455, 630 - 650,

670 690, 790 810, 890 910, 930 980, 950-970, 1150-1260, 1450, 1950, and 2250 nmwere separated from the 300 to 2400 nm data collected by the spectroradiometer. The indiceslisted in Table 1 were computed. The indices and reflectance data were imported into SAS(S.A.S. Institute Inc., Cary, North Carolina) for linear regression and correlation to plant watercontent.

Results

Corn and spinach had similar reflectance response when placed under water deficiency stress,while snap beans exhibited less reflectance change (Table 2). Figures 1, 2 and 3 showexamples of the hyperspectral reflectance profile of each species at two different plant watercontent levels.

Table 2. Example of differences in reflectance for corn, spinach, and snap beans placed underwater deficiency stress. Sample responses are typical of study population.

Difference in reflectance (percentage points)Wave band

Corn Spinach Snap Bean

630-650 2.50 4.0 4.0

950-970 3.0 11.0 1.0

1150-1260 2.5 14.0 1.0

1450 8.0 11.0 2.0

1950 6.5 6.0 2.0

2250 10.0 20.0 2.0

Figure 1. Typical hyperspectral reflectance profile for corn at two plant water content levels.

Corn

0.05.010.015.020.025.030.025.040.0

350 600 850 1100 1350 1600 1850 2100 2350Wavelength, nm

Reflectance,

%

PWC = 84%PWC = 93%


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Figure 2. Typical hyperspectral reflectance profile for spinach at two plant water content levels.

Figure 3. Typical hyperspectral reflectance profile for snap beans at two plant water contentlevels.

Significant correlations (p < 0.01) occurred between PWC and 1450 nm, 2250 nm, WI, and fWBIin corn, 950 - 970 nm, 1150 1250 nm, 1450 nm, 1950 nm, 2250 nm, and WI in spinach, and1450 nm and WI in snap beans. Figures 4, 5 and 6 indicate the relationship between each ofthese significant water bands or indices and plant water content. The statistical linearregression significance and coefficients of determination of each water band and indexcorrelated to the plant water content are presented in Table 3.

Snap Beans

0.010.020.030.040.050.060.0

350 600 850 1100 1350 1600 1850 2100 2350Wavelength, nm

R

eflectance,

%

PWC =89%PWC =78%

Spinach

0.010.020.030.040.050.060.0

350 600 850 1100 1350 1600 1850 2100 2350Wavelength, nm

Reflect

ance,

%

PWC = 92%PWC = 66%


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Figure 4. Relationship between plant water content, water band reflectance, and indices valuefor Corn

Plant Water Content vs. Water Band Reflectance, Corn

82.084.086.088.090.092.094.096.0

0.00 5.00 10.00 15.00 20.00 25.00Reflectance, %

PlantW

aterContent,%

1450

2250

Plant Water Content vs. Index Value, Corn

82.084.086.088.090.092.094.096.0

1.03 1.04 1.05 1.06 1.07 1.08Index Value

PlantWaterContent,%

WI

fWBI


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Figure 5. Relationship between plant water content, water band reflectance, and indices valuefor Spinach

Plant Water Content vs. Water Bands, Spinach

60.065.070.075.080.085.090.095.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0Reflectance, %

PlantWaterContent,%

950-9701150-1260145019502250

Plant Water Content vs. WI, Spinach

y = 9.47WI - 9.019r2 = 0.70

60.065.070.075.080.085.090.0

1.02 1.025 1.03 1.035 1.04 1.045 1.05 1.055 1.06Index Value, WI

PlantWaterContent,%


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Figure 6. Relationship between plant water content, water band reflectance, and indices valuefor Snap Beans

Table 3. Coefficients of determination (r2) of plant water content with average spectralreflectance and indices

Coefficient of Variation (r2) between PWC and average spectral reflectance

960 10 nm 1150 1260nm

1450 10nm

1950 10 nm 2250 10 nm

Corn 0.18 (4) 0.32 (3) 0.67 (1) 0.29 (3) 0.61 (1)Spinach 0.94 (1) 0.93 (2) 0.85 (2) 0.80 (2) 0.79 (2)Snap Beans 0.14 (3) 0.07 (4) 0.50 (1) 0.22 (3) 0.02 (4)

Coefficient of Variation (r2) between PWC and indices

WI NDVI SIPI fWBI WI/NDVI

Corn 0.43 (2) 0.0002 (4) 0.09 (4) 0.42 (2) 0.002 (4)Spinach 0.70 (2) 0.11 (4) 0.51 (3) 0.03 (4) 0.002 (4)Snap Beans 0.35 (1) 0.06 (4) 0.18 (3) 0.0006 (4) 0.02 (4)

r2statistical significance p:

1p< 0.001,

2p < 0.01,

3p0.05.

Plant Water Content vs. Average R1450, Snap Beans

y = -0.871R1450 + 0.962r2 = 0.50

60.065.070.075.080.085.090.095.0

5.0 10.0 15.0 20.0 25.0Reflectance at 1450 nm, %

PlantWaterContent,%

Plant Water Content vs. WI, Snap Beans

y = 3.442WI - 2.713

r2 = 0.35

60.065.070.075.080.085.090.095.0

1.01 1.02 1.03 1.04 1.05

Index Value, WI

PlantWaterContent,%


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Visual inspection of the desiccating snap bean flats revealed indications of wilt after five days(second sampling day) with an 85% PWC and permanent wilt occurred after seven days (thirdsampling day) with a 79% PWC. Permanent wilt in corn was observed on the second samplingday with an 85% PWC. Permanent wilt in spinach was observed on the third sampling day withan 88% PWC.

Discussion and Conclusions

Corn, spinach and snap beans follow the expected reflectance profile by exhibiting a higherreflectance level in the water absorption bands at lower plant water content (Figures 1 3).Corn and spinach exhibit a more distinct difference in reflectance compared to snap beans. Thepercent PWC loss in corn and snap beans were similar (9% and 11%, respectively).

For corn and snap beans, the water absorption band at 1450 nm appears to provide the mostaccurate estimate of plant water content (r2 = 0.67 and 0.50, respectively). The weakcorrelations at other bands indicate that this spectral response may be a weak indicator of plantwater content. Reflectance measurements in spinach are quite sensitive to plant water content,particularly in the 950 970 nm and the 1150 1260 nm bands (r2 = 0.94 and 0.93). Noimprovement could be found for these three species for estimating plant water content by usingindices or ratios over average reflectance data. While NDVI may be a good indicator of nitrogencontent and biomass at the canopy level, at the plant level it does not provide an estimate ofplant water content in these three species (r2 < 0.12).

WI and fWBI provided essentially the same estimate of plant water content in corn (r2 = 0.43and 0.42). Little shift occurred in spectral absorption during plant desiccation. However, inspinach and snap beans, WI and fWBI were significantly different with WI having a strongercorrelation with plant water content (r2 = 0.70 and 0.35). SIPI and WI normalized with NDVIwere not significant indicators of plant water content in any of these three species in this study.

The use of reflectance data appears to have promise for use at the plant level in estimatingplant water content in corn and spinach but less promising in snap beans. This techniquecombined with a physiological study of the plant water content at the permanent wilt stage may

allow growers to improve decisions concerning the application of water and other nutrients inthese commercial crops.

Acknowledgements

The authors recognize and appreciate support through funding from the United StatesDepartment of Agriculture in the form of a Special Research Grant number 2003-06134.

The use of trade names is only meant to provide specific information to the reader, and does notconstitute endorsement by Oklahoma State University.

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Chandler, C., P. Cheney, P. Thomas, L. Trabaud, and D. Williams. 1983. Fire in forestry. ForestFire Behaviour and Effects Vol 1.


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