Grass Water Stress

8/11/2019 Grass Water Stress

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Jos L. Chvez, Saleh Taghvaeian

Civil & Environmental Engineering De

Colorado State University

3/30/2012

Grass Water Stress and ET Monitoring

Using Ground- and Airborne-based

Remote Sensing: Project Report


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Grass Water Stress and ET Monitoring Using Ground- and Airborne-based

Remote Sensing

JosL. Chvez1and Saleh Taghvaeian

2

1

Assistant Professor, Department of Civil and Environmental Engineering, Colorado State University, 1372Campus Delivery, Fort Collins, CO 80523. Ph (970) 491-6095, Email: [email protected]

2 Postdoctoral Fellow, Department of Civil and Environmental Engineering, Colorado State University, 1372

Campus Delivery, Fort Collins, CO 80523. Ph (970) 491-3381, Email: [email protected]

Executive Summary

The purpose of this project was to using two levels or scales of remote sensing (ground- and

airborne-based) along with a crop water stress index (CWSI) and weather data to monitor grass

water stress and water use (evapotranspiration, ET) for the different treatments designed by the

Northern Colorado Water Conservancy District (NCWCD) during the summer of 2011. Remotesensing data were acquired during a 2-month period, from mid-July to mid-September, 2011.

The remotely sensed data and CWSI algorithm were able to quantify the variation in turfgrass

water use caused by the difference in the types of grasses and irrigation amounts. Grass water

stress through the CWSI was estimated over each experimental plot by establishing upper and

lower limits of canopy-air temperature differences. A similar lower limit relationship between

dT and vapor pressure deficit (VPD) was developed for Tall Fescue and Kentucky Bluegrass. In

addition, the validation of upper limit estimates with the actual field measurements showed that

the implemented model had an acceptable accuracy. Under a significantly reduced amount of

applied water, warm season grasses showed a relative smaller average CWSI of 0.41. On the

other hand, Fescue cultivars depicted a larger water stress, with an average CWSI that reached 1

(100% stress). Treatments without added organic amendment showed less water stress than

treatments with added compost at two rates. During the 2-month period, the grass-based

reference ET (ETo) was 302 mm. Over the same period the maximum total water consumption of

turfgrass treatments estimated based on the CWSI method was 274 mm while an energy balance

based model indicated that the maximum ET, during the same period, was 291 mm. This result

seems to validate the use of the CWSI method for monitoring grass water stress and water use.

On average, grass water use estimates using the CWSI method were about 11% smaller than the

results of the surface energy balance model. However, identifying the right time (during the day)

of remote sensing data acquisition seems to be critical for a successful application of the CWSImodel. Further research is suggested to not only identify the optimal window of opportunity to

acquire remote sensing data but to also develop an algorithm to adjust the data when acquired

outside of the optimal time of the day. The CWSI method (and/or the surface energy balance

method) has an enormous potential to be used in urban areas to improve turfgrass/lawns water

management and conservation practices.
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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1. Introduction

Canopy temperature has been utilized extensively in evaluating turfgrass water stress. One

method of using canopy temperature to evaluate stress development is known as Crop Water

Stress Index (CWSI), which provides a numerical representation of stress severity (Idso et al.,

1981; Jackson et al., 1981). The values of CWSI range between 0.0 and 1.0, with 0.0representing no stress and 1.0 representing maximum stress conditions. Although originally

developed for agricultural crops, CWSI approach has been also used in studying different species

of turfgrass, such as Bermudagrass (Carrow 1989), Centipedegrass (Carrow 1989), Creeping

Bentgrass (Martin et al., 1994), Kentucky bluegrass (Throssell et al., 1987; Martin et al., 1994),

Tall Fescue (Al-Faraj et al., 2001; Payero et al., 2005), and Zoysiagrass (Carrow 1989). In this

study, remotely sensed data on canopy spectral reflectance and temperature were collected from

ground-based and airborne platforms and then combined with in-situ weather data to identify

CWSI and grass water use or evapotranspiration (ET) in several species of turfgrass, under

varying conditions of soil preparation and irrigation amounts application. Finally, the results

were compared with estimates of an independent surface energy balance model that was

performed using the same ground-based remote sensing data.

2. Methods and Materials

2.1 Project Procedure

The study was conducted at NCWCDs Conservation Gardens (Latitude: 40.322 N, Longitude:

105.077 W, Elevation: 1,548 m above mean sea level), located at NCWCDs headquarter in

northern Colorado, near the city of Berthoud. Four grass sites (all designed and implemented by

NCWCD) were used in this study. The first site (C3, Figure 1) was a circular plot (diameter 15m) of Kentucky Bluegrass (Poa pratensisL.) that included an automated weather station. This

solar-powered weather station was equipped with sensors measuring several weather variables

including air temperature and humidity (Vaisala, Helsinki, Finland), wind speed and direction

(R. M. Young Company, Michigan, USA), solar radiation (LI-COR Biosciences, Nebraska,

USA), and precipitation. Measured parameters were processed and recorded using a data-logger

(Model CR1000, Campbell Scientific, Utah, USA). The second site (C4, Figure 1) was a

Kentucky Bluegrass site, designed and implemented by NCWCD, to investigate the effects of

soil preparation and organic amendment on the quality of Kentucky Bluegrass. This site

included two treatment of shallow and deep tillage (approximate depths of 0.15 m and 0.38 m)

and three treatments of organic amendments (0.0, 247, and 494 m3

ha-1

of plant waste compost),each with two replicates. The third site (D3, Figure 1) was a Tall Fescue (Festuca arundinacea

L.) site under two treatments of different irrigation amounts. The first treatment received water

in rates similar to what was estimated by the weather station for a grass-based reference surface.

The second treatment, however, received reduced amounts of water. Finally, the fourth site (D5,

Figure 1) was designed to explore the effects of different irrigation depths/uniformity on the


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quality of five turfgrasses species, namely: Tall Fescue, Fine Fescue (Festuca tenuifoliaL.),

Texas Hybrid Bluegrass (Poa arachniferaL.), Aggressive Kentucky Bluegrass, and a mixture of

two warm season grasses, namely Blue Grama (Bouteloua gracilisL.) and Buffalograss

(Bouteloua dactyloidesL.). At this site, sprinklers were installed only at the south end of the

experimental plots, resulting in a depth of applied water that decreased toward the North.

Several tipping-bucket rain gauges were installed, by NCWCD, in each plot at equally-spaced

locations from the sprinklers to measure the amounts of irrigation water received at each

location. Each rain gauge was assigned a number that increased with the distance from the

sprinkler. Table 1 presents a list of the experimental treatments, along with their abbreviations

that were used in the body of this report.

Figure 1. The location of NCWCDs headquarter (true color image, left) and the location of research sites within the

Conservation Gardens (false color composite image, right).


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Table 1. Grass type and depth of applied water (irrigation + precipitation) over the study period.

Site Treatment Abb. I+P (mm)

Tall Fescue Review (D3) Irrigation: full D3-If 322.0Irrigation: deficit D3-Id 313.0

Line Source Irrigation (D5)

Warm Season Mix, TBRG1# 2 D5-WSM-2 237.0

Warm Season Mix, TBRG # 4 D5-WSM-4



Agg. Kentucky Bluegrass, TBRG # 2 D5-AKB-2 237.0

Agg. Kentucky Bluegrass, TBRG # 4 D5-AKB -4



Texas Hybrid Bluegrass, TBRG # 2 D5-THB-2 237.0

Texas Hybrid Bluegrass, TBRG # 4 D5-THB -4



Fine Fescue, TBRG # 2 D5-FF-2 237.0

Fine Fescue, TBRG # 4 D5-FF-4



Tall Fescue, TBRG # 2 D5-TF-2 237.0

Tall Fescue, TBRG # 4 D5-TF-4



Soil Preparation (C4)

Tillage: deep; Compost: high C4-Td/Ch 305.0

Tillage: deep; Compost: low C4-Td/Cl 305.0

Tillage: deep; Compost: no C4-Td/Cn 305.0

Tillage: shallow; Compost: high C4-Ts/Ch 305.0

Tillage: shallow; Compost: low C4-Ts/Cl 305.0

Tillage: shallow; Compost: no C4-Ts/Cn 305.0

Weather Station (C3) NA C3-WS 375.0

1Tipping-bucket rain gauge


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2.2 Remote sensing data

Multispectral remote sensing data were obtained at two different scales, point (ground-based)

and distributed (airborne). At ground-based level, a hand-held, multi-spectral radiometer (model

MSR5, CROPSCAN, Inc., Rochester, MN) was used to measure surface reflectance in five

wavebands similar to the wavebands of the sensors onboard Landsat 5 Thematic Mapper (TM)satellite. These bands were in the blue (TM1), green (TM2), red (TM3), NIR (TM4) and short-

wave infra-red (SWIR, TM5) parts of the EM spectrum. The MSR5 sensor has two sets of optics

with 28 field of view (FOV). One set of optics (five bands) was placed looking downward to

detect the radiance reflected from the surface and the other was placed looking upward, through

an opal glass cosine diffuser, to estimate the incoming radiance in the same bands. Target

reflectance in each of the five bands is estimated by an internal program through dividing the

downward by upward measured radiance. An infra-red thermometer or IRT (model IRt/c.2,

Exergen Corp., Watertown, MA) with a 35 FOVwas also attached to the MSR5 to measure

turfgrass canopy temperature.

The reflectance and temperature of turfgrass canopy were measured within two hours from solar

noon, on seven dates (July 20, Aug 12 and 22, and Sept. 8, 13, 19, and 22).

Airborne images were acquired during three campaigns using the Utah State University

multispectral airborne digital remote sensing system. This system consisted of three Kodak

Megaplus digital frame cameras with band-pass filters similar to those of the MSR5 and Landsat

5, TM2 (green), TM3 (red), and TM4 (NIR). The system included a thermal camera (model

SC640, FLIR systems, Boston, Massachusetts, USA), which detects the incoming radiation in

the long-wave infra-red part of the electromagnetic (EM) spectrum (similar to TM6). The three

aircraft overpasses occurred before and after solar noon (Table 2). Flight elevation was about457 m above ground level, resulting in pixel sizes of 0.2 and 0.6 m in the visible/NIR and

thermal bands, respectively. For multi-spectral cameras, the shutter speed was 7 milliseconds

and the F-stop (relative aperture) was 5.6, 5.6, and 8 for TM2, TM3, and TM4 cameras,

respectively. During the afternoon flight on August 31st, clouds covered the study area and

acquired imagery was not used in the analysis discussed in this report.

Table 2. Airborne remote sensing flight information. Times are in Mountain Standard Time (MST).

Date Solar noon Before noon flight After noon flight

07/19/2011 13:06 12:31 14:27

08/12/2011 13:05 11:55 13:4208/31/2011 13:00 11:45 14:47

Differences in spectral characteristics among turfgrass species were also investigated by

analyzing the data on canopy temperature and reflectance at different wavebands. In addition,


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two widely-used vegetation indices (VIs) were also computed. These two VIswere NDVI

(Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index):

(1)

(2)

where TM3 and TM4 are reflectance (in decimals) in red and NIR portions of the EM spectrum,

respectively, and L is a coefficient that decreases with the increase in vegetation density. As per

suggestion of Huete (1988), a constant L value of 0.5 was used in this study. Although NDVI

has been extensively used in turfgrass studies, its value is sensitive to the changes in surface

wetness. To account for this issue, SAVI was developed in a fashion to be more resilient to

surface wetness variation (Huete 1988), a characteristic that was confirmed by numerous studies

(Glenn et al., 2011).

2.3 Crop Water Stress Index

Crop Water Stress Index (CWSI) approach was developed in 1981, when Idso et al. (1981) and

Jackson et al. (1981) proposed the empirical and theoretical methods of estimating CWSI,

respectively. According to Idso et al. (1981), CWSI can be estimated using equation 3.

(3)

where dT is the temperature difference between canopy and air (TcanopyTair) and subscripts m,

LL, and UL represent measured, lower limit, and upper limit of dT, respectively. Since all

variable have the same units, CWSI is a dimensionless ratio. The lower limit of dT occurs under

non-water-stressed conditions when ET is only limited by atmospheric demand. On the other

hand, the upper limit of dT is reached under non-transpiring conditions when ET is stopped due

to the lack of water. Idso et al. (1981) proposed that under non-water-stressed conditions the

lower dT limit is a linear function of the air vapor pressure deficit (VPD, kPa):

dTLL= a + b VPD (4)

where a is the intercept and b is the slope of the linear relationship. Similarly, the upper

limit can be expressed as a linear function of vapor pressure gradient (VPG, kPa).

dTUL= a + b VPG (5)

where a and b are the same coefficients and VPG is the difference between saturated vapor

pressure at air temperature and at a higher temperature equal to air temperature plus the

coefficient a. dTULcan also be determined by measuring dT over a severely stress plant area

(turfgrass in this case). To verify the dTULconcept a grass plot was sprayed to bring ET to zero.


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In this study, dTmwas calculated using turfgrass canopy temperature detected by the mobile or

handheld IRT (IRt/c.2) sensor and air temperature measured at about the same time at the on-site

weather station (Site C3). The lower limit of dT was determined by plotting VPD data vs. dTm

values that were collected over healthy (non-stressed) turfgrass within two days after an

irrigation or precipitation event. The upper limit of dT was estimated using the proposed method

of Idso et al. (1981). In addition, estimated dTULvalues were compared with the actual dT

measurements over a severely stressed (non-transpiring) patch of turfgrass to validate the

accuracy of this approach.

2.4 Evapotranspiration

Turfgrass ET was estimated based on two independent approaches. The first approach was

based on the CWSI concept. Jackson et al. (1981) showed that there is a unique mathematical

relationship between CWSI and the ET of the studied vegetative surface. The equation derived

by Jackson et al. (1981) can be rearranged into the following format:

ETa= (1CWSI) ETp (6)

where ETais actual ET and ETpis potential ET (grass-based reference ET, EToin this case).

Equation 6 was used as the first approach to estimating grass ET using ground-based and

airborne remote sensing derived CWSI. The second implemented approach was a remote

sensing energy balance (RSEB) model known as SEBAL (Surface Energy Balance Algorithm for

Lands, Bastiaanssen et al. 1998). Similar to other RSEB models, SEBAL is based on the simple

form of energy balance equation at studied surfaces:

Rn= G + H + LE (7)

where Rnis net radiation, G is soil heat flux, H is sensible heat flux, and LE is the latent heat flux

in units of energy (W m-2

) or depth of water (mm day-1

). The Rn, G, and H components are

modeled by integrating remotely sensed and in-situ data, and LE is calculated as the residual of

the above equation. SEBAL offers an innovative method for calculating H, in which spatially

distributed values are approximated iteratively by identifying two extremepixels. One of the

extreme pixels is a hot/dry pixel (e.g., bare soil), where all of the available energy is used for

heating the soil and the air above the surface. As a result, the value of H over this pixel is equal

to the available energy (H = RnG). The other extreme pixel is a cold/wet pixel (e.g., water),

where the available energy is used in transforming the physical state of water from liquid to

vapor, resulting in a negligible H (H = 0). The value of H over all other pixels could be

interpolated between these two extreme conditions. In this study, the SEBAL model was applied

to the ground-based remote sensing data.


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3. Results and discussion

3.1 Remote sensing data

Remotely sensed data collected by the MSR5-IRt/c.2 sensor had values similar to those reported

in the literature for turfgrass surfaces (e.g., Trenholm et al., 2000; and Fitz-Rodriguez and Choi,2002). In the visible part of the EM spectrum, average spectral reflectance of all experimental

plots was less than 6.7, 9.0 and 12.0% of the incoming shortwave radiation in TM1, TM2, and

TM3 wavebands, respectively.

As expected, turfgrass plots with lower quality (higher visible and SWIR and lower NIR

reflectance) had higher surface temperatures. Minimum and maximum average temperatures

were both detected over Tall Fescue plots of the D5 site, with values of 28.6 and 42.5 (C) over

D5-TF-4 and D5-TF-8 plots, respectively. Table 3 summarizes the average value of remotely

sensed spectral characteristics of the experimental plots for the seven dates of data collection. In

addition, Figures 2 and 3 demonstrate box plots of some of these parameters to facilitate a bettercomparison among treatments.

3.2 Crop Water Stress Index

3.2.1. Upper and Lower dT limits

Several researchers (e.g. Martin et al., 1994) have reported that each turfgrass species has a

unique dTLL-VPD relationship, regardless of the variability in the cultural and climatological

conditions. Despite such species-dependence, Carrow (1993) stated that these relationships are

similar enough to be combined for a practical purpose such as irrigation scheduling. In this

study, the dTLL-VPD relationship was developed using the data collected within two days of anirrigation and/or precipitation event over two experimental plots (D3-If Tall Fescue and C4

Kentucky Bluegrass) that had the highest values of NDVI and did not show sign of stress.

Tall Fescue: dTLL= 10.293.31 VPD, (r2= 0.97) (8)

Kentucky Bluegrass: dTLL= 10.363.27 VPD, (r2= 0.98) (9)

Interestingly, the developed relationships were almost identical. However, the slope and

intercept of the above equations are larger than the values reported by Al-Faraj et al. (2001) for

Tall Fescue and by Throssell et al., (1987) for Kentucky Bluegrass. As illustrated in Figure 4, the

cumulative amount of applied water at both plots was always larger than EToover the studyperiod. It seems that obtaining larger coefficients in this study is most probably due to the

difference in climatological conditions. Previous studies have also showed that the slope of

dTLL-VPD relationship is a larger negative number under arid/semi-arid compared to humid

climates (Carrow 1993).


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Table 3. Turfgrass reflectance (%) in different wavebands, along with NDVI/SAVI (-) and surface temperature (C)

over experimental plots, averaged for the seven dates of data collection.

Plot TM1 TM2 TM3 TM4 TM5 NDVI SAVI Tsurf, C

D3-If 2.17 3.99 2.77 48.10 18.90 0.89 0.67 28.97

D3-Id 2.21 3.82 2.74 46.81 17.76 0.89 0.66 29.17

D5-WSM-2 4.46 7.41 6.60 34.86 26.84 0.68 0.46 31.06

D5-WSM-4 4.59 7.43 6.80 34.24 26.65 0.67 0.45 30.67

D5-WSM-6 4.76 7.75 7.34 33.30 27.32 0.64 0.43 31.83

D5-WSM-8 5.10 7.85 7.99 29.42 28.52 0.57 0.37 34.54

D5-AKB-2 3.06 5.18 4.81 36.92 26.87 0.77 0.53 33.64

D5-AKB -4 3.09 5.12 4.86 35.10 26.53 0.76 0.50 32.89

D5-AKB -6 3.19 5.25 4.99 36.15 26.95 0.76 0.51 32.82

D5-AKB -8 4.83 7.29 8.56 30.04 33.06 0.56 0.36 36.70

D5-THB-2 2.63 4.71 3.68 43.17 24.04 0.84 0.61 30.24

D5-THB -4 2.83 4.86 4.12 38.96 24.84 0.81 0.56 31.08

D5-THB -6 3.89 6.24 6.10 37.36 28.60 0.72 0.50 33.27

D5-THB -8 5.04 7.53 8.60 31.59 33.39 0.57 0.38 36.50

D5-FF-2 2.91 4.75 5.00 33.57 27.45 0.74 0.48 34.67

D5-FF-4 3.49 5.40 6.00 32.33 29.63 0.69 0.45 36.04

D5-FF-6 3.97 5.96 6.76 32.02 31.35 0.65 0.43 36.15

D5-FF-8 6.57 8.93 11.77 25.89 41.14 0.38 0.24 43.50D5-TF-2 2.31 4.05 3.36 39.79 18.78 0.84 0.59 28.86

D5-TF-4 2.51 4.27 3.83 37.38 19.47 0.81 0.55 28.64

D5-TF-6 3.79 5.85 6.41 32.40 24.93 0.67 0.44 32.30

D5-TF-8 6.66 8.99 11.81 22.65 37.28 0.32 0.19 42.47

C4-Td/Ch 2.50 4.35 3.41 45.46 23.33 0.86 0.64 31.80

C4-Td/Cl 2.25 3.94 2.98 45.46 22.49 0.88 0.65 31.93

C4-Td/Cn 2.05 3.67 2.59 47.63 21.50 0.90 0.67 29.82

C4-Ts/Ch 2.23 3.85 2.99 44.99 22.17 0.87 0.64 32.66

C4-Ts/Cl 2.22 3.84 3.03 44.08 22.12 0.87 0.63 32.96

C4-Ts/Cn 2.00 3.51 2.49 46.76 20.85 0.90 0.67 31.08

C3-WS 2.45 4.51 3.34 44.53 22.88 0.86 0.63 32.37


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Figure 3. Turfgrass NDVI (top), SAVI (middle), and surface temperature (bottom).


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Figure 4. Cumulative amounts of reference EToand irrigation/precipitation (I + P) over D3-If and C4 plots, all in

units of water depth (mm).

Given the similarity between the two dTLL-VPD equations developed in this study, they were

combined (averaged) and a single equation (dTLL= 10.33.3 VPD) was used to estimate the

lower limit of dT over the research plots. The dTLL-VPD coefficients were used to predict the

upper dT limit. Resulted dTULvalues had a rather constant pattern, ranging from 17.0 to 21.9 C

over the seven dates of data collection. To evaluate the accuracy of Idso (1981) method,

estimated dTULvalues were compared with dTULvalues that were measured over a severely

stressed (non-transpiring) turfgrass patch. Based on this comparison, the mean bias error (MBE)

of modeled dTULwas 4.0 C. A closer investigation revealed that the error was influenced by

three measured dTULvalues that were significantly lower than expected. These threemeasurements were taken over the non-transpiring turfgrass patch on dates when the adjacent

D3-Id plot had been recently irrigated. Since the plot surface slope was toward the dry patch, the

lower measured dTULseems to have been produced by irrigation water runoff from the D3-Id

plot which might have lowered the surface temperature at the dry plot/path area. Excluding these

three points from the analysis, the MBE of the modeled dTULwas reduced to only 0.7 C. Figure

5 shows measured as well as modeled limits of dT over the four plots of warm season grass mix.

As expected, measured dT falls within the range identified by the upper and lower limits. In

addition, measured dT values were closer to the dTULas the distance from sprinklers increased.

3.2.2. CWSI computation using ground-based remote sensing inputs

Using modeled upper and lower dT limits and measured dT, CWSI was estimated for each of the

turfgrass plots on the 7 dates of ground-based remote sensing data collection. Except for the D5-

FF-8 and D5-TF-8 plots, estimated CWSI had a range of values between -0.1 and 0.8 (Figure 6).

150

250

350

450

550

7/20 8/10 8/31 9/21

Depthofwater

(mm)

D3-If (I+P)

C4 (I+P)

ETo


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Figure 5. Values of dTm(gray lines) and dTLL/dTUL(black lines) over D5-WSM plots.

As explained before, these two plots are located at larger distances from the sprinkler and thus

received the least amount of water. In addition, turfgrass was not at full cover at these locationsand part of the background soil was viewed by the IRT sensors. CWSI values for the farthest

distance of the other species (D5-WSM-8, D5-AKB-8, and D5-THB-8) were also larger than

their corresponding plots at a closer proximity to the sprinklers.

To compare the water status of each turfgrass species in site D5, CWSI values were averaged

over the two plots that were closer to the sprinkler and over the two plots that were farther from

sprinklers. Among studied species, the CWSI averaged for the first two plots was larger (0.44)

over Fine Fescue and smaller (0.02) over Tall Fescue. For the second two plots (farthest from

sprinklers) Fine Fescue still had the largest average CWSI (0.74), while the lower value (0.32)

belonged to the warm season mix. These results suggest that for the same amount of limitedirrigation (assuming similar sprinkler uniformities), Fine Fescue and warm season species have

the lowest and highest stress tolerance, respectively. This is not surprising as warm season

species are expected to perform better under hot and dry conditions. In addition, the range of

CWSI variation with distance from sprinklers was smaller for warm season mix (0.26) and larger

for Fine and Tall Fescue (0.63 and 0.91, respectively), confirming that the latter two species are

highly sensitive to the amount of applied water.

Within the soil preparation site (C4), plots with deeper tillage had a smaller CWSI compared to

plots with shallower tillage (0.17 vs. 0.23). This is probably due to the fact that a deeper tillage

destroys compacted soil layers and improves water movement and aeration in the root zone, thusproviding a more favorable growth environment. Among each tillage depth treatment, however,

the plots with no organic amendment had surprisingly smaller CWSI compared to the plots with

added compost. There was no significant difference in CWSI between the two rates of compost

application. Within site D3, the two treatments of irrigation amount showed almost no sign of

stress, with average CWSI values of 0.05 and 0.07 over D3-If and D3-Id plots, respectively.

-4

0

4

8

12

16

20

24

7/20 8/10 8/31 9/21

dT(C

)

D5-WSM-2 D5-WSM-4 D5-WSM-6 D5-WSM-8


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Figure 6. Box plots of estimated CWSI (in decimals) over studied turfgrass plots.

Such a negligible difference is due to the fact that both plots received almost the same amount of

irrigation water. In addition, the sum of irrigation and precipitation was larger than EToover the

study period, which explains why estimated CWSI was close to zero. In other words, the D3-Id

plot seems to not have been under a significant deficit irrigation practice.

3.2.3. CWSI computation using airborne remote sensing data

Models for identifying upper and lower dT limits that were developed using ground-based

remote sensing data were also applied to the airborne imagery to estimate CWSI on a distributedbasis (Figure 7). The results showed a significant variation in CWSI within each plot. However,

the general pattern was similar to the results of ground-based remote sensing. Over the D5 site,

CWSI values varied from almost zero at the south end to unity at the north end on July 19th

and

August 12th

flights (note that sprinklers were installed at the south end). Smaller CWSI values

were calculated on August 31stdue to the cooler surface temperature. Lower temperatures were

caused by a combination of: wind gusts (> 4.0 m s-1

) at the overpass time and irrigation events

that were either occurring at the time of the flight or occurred the day before.

3.3 Evapotranspiration

Turfgrass actual ET (ETa) was estimated using two different approaches, i.e. the CWSI and the

SEBAL model. For the seven dates of ground-based remote sensing data collection, average

daily ETawas very similar between the two methods. This result suggests that the CWSI can be

used as an effective alternative to the SEBAL model in the estimation of grass ET a. The average

daily ETahad a range of values from 0.0 to 4.0 mm d-1

.


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Figure 7. False color airborne image of the study area (a) and maps of CWSI, ranging from zero (blue) to unity

(Red), for each overpass time: 07/19/2011 before (b) and after (c) solar noon, 08/12/2011 before (d) and after (e)

solar noon, and 08/31/2011 before solar noon (f).

The average grass reference ETowas 4.5 mm d-1

during the same period. Based on both

methods, D5-FF-8 and D5-TF-8 plot had the smallest ETa, while D5-TF-2 and D5-TF-4 plots

had the largest average daily ETa. Within the D5 site, the average SEBAL-ET estimates over all

distances from sprinklers had the following highest to lowest order: D5-WSM, D5-AKB, D5-

THB, D5-TF, and D5-FF. SEBAL estimates also confirmed the results of CWSI for the C4 site.

For this site, the plots with no organic amendment had larger ETarates compared to those thatreceived different rates of compost. The average daily ETaover the C3-WS (weather station)

plot was 3.2 and 3.4 mm d-1

based on the CWSI and SEBAL methods, respectively. These

estimates were similar to the lowest ETaestimates over C4 site, which had the same turf species

(Kentucky Bluegrass) but received about 70 mm less irrigation water during the two months of

the study period. Thus, it seems that factors other than turf species and irrigation amount may

(a) (b) (c)

(d) (e) (f)


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have caused the observed ETarates to be lower than expected over C3-WS plot. Average daily

SEBAL-ETawas the same over the two plots of D3 sites, which is consistent with their similar

surface spectral characteristics.

Daily ETawas calculated for each day during the study period by assuming that the ratio of

actual to reference ET (actual crop coefficient, Kca) varied linearly between two consecutive datacollection dates. Therefore, this ratio or Kcawas interpolated between remote sensing days and

were multiplied by the alfalfa reference ET (ETo) to obtain an ETavalue for each day during the

study period. Resulted daily ETavalues were then summed up to provide an estimate of the total

ETaover the two months (seasonal) of the study. The maximum total (seasonal) ETaresulted

from the D5-TF-4 plot with 274 and 291 mm based on the CWSI and SEBAL methods,

respectively. Total ETo, from the weather station data, was 302 mm over the same period. The

minimum total ETawas also estimated over the D5-FF-8 and D5-TF-8 plots. On average, CWSI

results were 15% smaller than the SEBAL estimates. However, the difference was larger over

plots with lower ETarates, which is most probably due to the lack of full canopy cover at these

plots. Since the majority of urban turfgrass systems (e.g., municipal parks, golf courses, athletic

fields, home gardens, etc.) maintain the turf at a full cover condition, all of the plots that had an

average NDVI of less than 0.6 were excluded from further comparison. This new analysis

reduced the difference between the two methods to 11%. This difference is acceptable by most

turf practitioners and may become even smaller as the study period expands to seasonal and

annual time frames. Table 4 presents average daily and total ETarates for the two implemented

methods (CWSI and SEBAL) in SI and English units. This table is followed by Figure 8, which

shows box plots of daily ETavalues for all of the days during the study period.

Turfgrass ETawas mapped by applying the CWSI approach to the airborne imagery. Daily ET

maps had a pattern similar to the pattern of ground-based CWSI-ET (Figure 9). For example, the

imagery acquired on August 12th

and 31stclearly show that ETawas larger in the middle part of

C4 site, where the treatments with no added compost were located. Although the plots with

organic amendments, located toward the north and south end of this site, had smaller ETarates,

the difference may not have been caused by the treatments as the pattern does not match the

rectangular shapes of plots. In addition, a gradient of ETavalues was observed over the D5 site,

where water use decreases rather rapidly from the south to the north end of the site. Figure 9

also illustrates a significant variation within each treatment, affirming one of the most important

drawbacks of point-based measurements, which is selecting a representative location.


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Table 4. Turfgrass daily average and total ETa(during the study period), based on CWSI and SEBAL methods.

PlotAvg. CWSI-ETa Total CWSI-ETa Avg. SEBAL-ETa Total SEBAL-ETa

mm (in) d-1

mm (in) mm (in) d-1

mm (in)

D3-If 4.0 (0.16) 263 (10.4) 3.9 (0.15) 275 (10.8)

D3-Id 4.1 (0.16) 261 (10.3) 3.9 (0.15) 270 (10.6)

D5-WSM-2 3.5 (0.14) 237 (9.3) 3.4 (0.14) 245 (9.7)

D5-WSM-4 3.7 (0.15) 240 (9.4) 3.6 (0.14) 248 (9.8)

D5-WSM-6 3.4 (0.13) 218 (8.6) 3.3 (0.13) 232 (9.1)

D5-WSM-8 2.6 (0.10) 167 (6.6) 2.8 (0.11) 200 (7.9)

D5-AKB-2 3.2 (0.12) 208 (8.2) 3.3 (0.13) 231 (9.1)

D5-AKB -4 3.4 (0.13) 210 (8.3) 3.5 (0.14) 234 (9.2)

D5-AKB -6 3.2 (0.13) 188 (7.4) 3.3 (0.13) 220 (8.7)

D5-AKB -8 1.9 (0.07) 95 (3.7) 2.1 (0.08) 137 (5.4)

D5-THB-2 3.8 (0.15) 258 (10.2) 3.9 (0.15) 266 (10.5)

D5-THB -4 3.8 (0.15) 258 (10.2) 3.9 (0.15) 271 (10.7)

D5-THB -6 3.3 (0.13) 221 (8.7) 3.2 (0.13) 233 (9.2)

D5-THB -8 2.1 (0.08) 146 (5.7) 2.2 (0.08) 176 (6.9)

D5-FF-2 2.7 (0.11) 182 (7.1) 3.0 (0.12) 216 (8.5)

D5-FF-4 2.2 (0.09) 129 (5.1) 2.5 (0.10) 173 (6.8)

D5-FF-6 2.3 (0.09) 145 (5.7) 2.5 (0.10) 175 (6.9)

D5-FF-8 0.4 (0.02) 10 (0.4) 0.2 (0.01) 15 (0.6)D5-TF-2 4.0 (0.16) 268 (10.5) 4.2 (0.16) 286 (11.2)

D5-TF-4 4.1 (0.16) 274 (10.8) 4.3 (0.17) 291 (11.5)

D5-TF-6 3.1 (0.12) 185 (7.3) 3.3 (0.13) 218 (8.6)

D5-TF-8 0.2 (0.01) 6 (0.2) 0.4 (0.02) 38 (1.5)

C4-Td/Ch 3.3 (0.13) 212 (8.3) 3.4 (0.13) 246 (9.7)

C4-Td/Cl 3.2 (0.13) 211 (8.3) 3.4 (0.13) 248 (9.7)

C4-Td/Cn 3.8 (0.15) 257 (10.1) 3.9 (0.15) 281 (11.1)

C4-Ts/Ch 3.1 (0.12) 205 (8.1) 3.3 (0.13) 248 (9.8)

C4-Ts/Cl 3.1 (0.12) 204 (8.0) 3.3 (0.13) 247 (9.7)

C4-Ts/Cn 3.6 (0.14) 238 (9.4) 3.6 (0.14) 268 (10.5)

C3-WS 3.2 (0.13) 209 (8.2) 3.4 (0.13) 249 (9.8)


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Figure 8. Box plots of estimated ET (mm.day -1) based on CWSI (top) and SEBAL (bottom).

Furthermore, maps of CWSI-ETaalso showed that the value of daily ETaresulting from this

method depended on the time of remote sensing data acquisition. In Figure 9, for example, the

after solar noon airborne remote sensing system overpass generated somewhat different values of

daily ETacompared to the before solar noon flight on both July 19th

and August 31st.

Vegetation water stress has been indicated to be better capture afternoon between 1 and 3 p.m.

Therefore, it is necessary to identify the time of the day at which remote sensing data acquisitionfor estimating CWSI and daily ETawould be more appropriate and develop a method for

extrapolating/adjusting CWSI values when the remote sensing data have been acquired at non-

ideal times of the day.


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Page 19

Figure 9. False color airborne image of the study area (a) and maps of daily ET (mm d -1), for each overpass time:

07/19/2011 before (b) and after (c) solar noon, 08/12/2011 before (d) and after (e) solar noon, and 08/31/2011 before

solar noon (f).

4. Conclusion

The results of this study indicate that variations in the amount of irrigation water applied caused

a difference in turfgrass multispectral response that was detected by the remotely sensed data.

Turfgrass canopy temperature as well as reflectance in the visible and SWIR wavebands had an

inverse, while the reflectance in the NIRband and VIs had a direct relationship with the amountof applied water. Among studied grass species, the fine and tall Fescue showed the largest

sensitivity to the different amounts of water applied. This result suggests that irrigation

scheduling criteria for Fescue cultivars should be defined more conservatively compared to other

species. On the other hand, a mix of two warm season grasses showed the largest tolerance to

limited irrigation (less stress was detected).

(a) (b) (c)

(d) (e) (f)


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Page 20

In this study, similar dTLL-VPD relationships were developed for Tall Fescue and Kentucky

Bluegrass. The coefficients of this relationship were used to estimate upper dT limit, using the

approach presented by Idso (1981). Modeled dTULvalues were within 10% of the actual values,

measured over a severely stressed turfgrass patch. The variations in estimated CWSI over

experimental plots were consistent with changes in canopy reflectance, confirming that under

reduced amounts of applied water, warm season and Bluegrass turf experienced less water stress

than the Fescue grass. Although a deeper tillage resulted in smaller CWSI values, treatments

with added levels of compost surprisingly showed a larger stress compared to plots with no

organic amendment.

In this study, CWSI results were used to calculate turfgrass water use (ET). To evaluate the

performance of the CWSI-ET method, the results were compared with the ET estimate of the

SEBAL method; which is a complex surface energy balance model. Over turfgrass plots that

were at or close to full cover (where CWSI method can be applied), CWSI-ET results were only

11% smaller than SEBAL estimates on average. Such a low difference is promising, since the

CWSI method has several advantages over the SEBAL model. The main advantage is that it

requires far less input data. Once the relationships for upper and lower dT limits are identified

for a specific region and vegetation type, the CWSI method can be applied using only one

remote sensing (surface temperature) and two weather station measured weather (air temperature

and vapor pressure) variables. However, the CWSI to be applied successfully with airborne

remote sensing images an appropriate time of the day needs to be identify to image acquisition in

conjunction with an algorithm to adjust the CWSI when the images had been acquired outside of

the ideal opportunity time.

5. References

Al-Faraj, A., Meyer, G. E., & Horst, G. L. (2001). A crop water stress index for tall fescue (Festuca arundinacea

Schreb.) irrigation decision-making - a traditional method. Computers and Elec. in Agriculture, 31, 107-124.

Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., & Holtslang, A. A. (1998). A remote sensing surface energy

balance algorithm for land (SEBAL): 1. Formulation. Journal of Hydrology, 212213, 198-212.

Carrow, R. N. (1989). Turfgrass irrigation scheduling by infrared thermometry. In Proceedings of the 1989 Georgia

Water Resources Conference, May 16-17, 1989, Athens, Georgia, 63-65.

Fitz-Rodriguez, E., & Choi, C. Y. (2002). Monitoring turfgrass quality using multispectral radiometry. Transactions

of the ASAE, 45(3), 865-871.

Glenn, E. P., Neale, C. M. U., Hunsaker, D. J., & Nagler, P. L. (2011). Vegetation index-based crop coefficients to

estimate evapotranspiration by remote sensing in agricultural and natural ecosystems. Hydrological Processes,

25, 4050-4062.

Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309.

Idso, S. B., Jackson, R. D., Pinter Jr., P. J., Reginato, R. J., & Hatfield, J. L. (1981). Normalizing the stress-degree-

day parameter for environmental variability. Agricultural Meteorology, 24(1), 45-55.


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Jackson, R. D., Idso, S. B., & Reginato, R. J. (1981). Canopy temperature as a crop water stress indicator. Water

Resources Research, 17(4), 1133-1138.

Martin, D. L., Wehner, D. J., & Throssell, C. S. (1994). Models for predicting the lower limit of the canopy-air

temperature difference of two cool season grasses. Crop Science, 34, 192-198.

Martin, D. L., Wehner, D. J., Throssell, C. S., & Fermanian, T. W. (2005). Evaluation of four crop water stress

index models for irrigation scheduling decisions on Penncross Creeping Bentgrass. International TurfgrassSociety Research Journal, 10, 373-386.

Payero, J. O., Neale, C. M. U., & Wright, J. L. (2005). Non-water-stressed baselines for calculating crop water stress

index (CWSI) for alfalfa and tall fescue grass. Transactions of the ASAE, 48(2), 653-661.

Throssell, C. S., Carrow, R. N., & Milliken, G. A. (1987). Canopy temperature-based irrigation scheduling indices

for Kentucky bluegrass turf. Crop Science, 27(1), 126-131.

Trenholm, L. E., Schlossberg, M. J., Lee, G., Parks, W., & Geer, S. A. (2000). An evaluation of multi-spectral

responses on selected turfgrass species. International Journal of Remote Sensing, 21(21), 709-721.

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