Estimating plant crown transpiration and water use efficiency by vegetative reflectance indices associated with chlorophyll fluorescence

Hidenori Furuuchi, Michael W. Jenkins, Randy S. Senock, James L. J. Houpis, James C. Pushnik

Department of Biological Sciences, California State University, Chico, USA.
Department of Earth and Environmental Sciences, East Bay, USA.
Department of Geological and Environmental Sciences, California State University, Chico, USA.

DOI: 10.4236/oje.2013.32015                

Abstract

This research developed estimates of plant crown transpiration and water-use-efficiency using reflectance and derivative indices extracted from remotely sensed chlorophyll fluorescence measurements under natural conditions. Diurnal changes of leaf-level gas exchange (carbon assimilation rate (A), stomatal conductance (g_s), transpiration rate (E)), chlorophyll fluorescence and canopy-scale remote sensing were measured on top crown of valley oak (Quercus lobata) in the foothills of central California, USA. The results indicated Q. lobata experienced saturating irradiance (PAR), which induced photoinhibition indicated by a decrease in the quantum efficiency of photosystem II (r² = 0.648 with F_v ′/F_m′ and r² = 0.73 with F_PSII) and open reaction centers (qP; r² = 0.699). The excess absorbed quantum energy was dissipated as heat through the Xanthophyll cycle and other processes (photorespiration and the water-water cycle) rather than energy emission as steady state chlorophyll fluorescence (F_s). An increase in leaf temperature caused by the activity of Xanthophyll cycle was correlated to a decrease in F_s (r² = 0.381) and an increase in evaporative cooling through E (r² = 0.800) and water use efficiency (WUE; r² = 0.872).

Keywords

Crown Transpiration; Remote Sensing; Chlorophyll Fluorescence; Reflectance; Quercus lobata

1. INTRODUCTION

Estimation of plant crown transpiration and wateruse-efficiency (WUE) based on remotely sensed vegetative indices has the potential to enhance the study of ecosystem water flux and how species and ecosystems could respond to future climatic induced water stresses. In their Fourth Assessment Report, the Intergovernmental Panel on Climate Change reported that climate change has induced variation in precipitation patterns globally during the last century; some regions such as eastern parts of Northern and South America, northern Europe and northern and central Asia have increased their precipitation while other areas such as the Shale, the Mediterranean, southern Africa and southern Asia have had reductions [1]. Long-term droughts have been observed and have affected agriculture and economic development in some semi-arid and sub-humid regions of the globe including the western U.S. [2]. It is projected that some of current water stressed areas will experience even more severe drought and an increase in the frequency of drought during the 21^st century [2,3].

Because the impacts of water stress vary in time, across space and between species, they generate shifts in species abundance of forest vegetation [4]. Water stress also changes vegetative water use patterns, such as WUE. These water stress impacts on vegetation gas exchange play a very significant role in the local to global carbon cycles [5,6]. Therefore, environmental stresses such as, heat, fire and insect stresses or vegetation water status have become the subject of remote sensing studies because spectral indices of vegetation can be informative about plant and ecosystem physiological conditions including atmospheric-terrestrial gas exchange processes.

Spectral reflectance indices commonly used for remote sensing are dependent on photosynthetic pigment concentration and plant water content [6-11]. In addition to these indices, reflectance indices associated with chlorophyll fluorescence have been successively used to estimate photosynthetic activities under heat and drought stress, chlorophyll fluorescence itself is linked to physiological stress in plants [12-17]. Currently, remote estimation of chlorophyll fluorescence has been proposed to integrate the physiological function at the ecosystem scale projecting net primary productivity (NPP). Quantum yield of PSII and the steady-state fluorescence (F_s) have been proposed for estimating the photosynthetic radiation use efficiency at large scales, suggesting remote sensing of chlorophyll fluorescence parameters as a tool for large scale CO₂ flux and transpiretion measurements [12].

In addition to the use of chlorophyll fluorescence for CO₂ flux measurements, Flexas et al. (2002) [18] demonstrated that F_s tracked stomatal conductance (g_s) rate of field-grown grapevines under drought conditions. However, this experiment was conducted at a single-leaf scale, not at a larger scale such as canopy, stand or ecosystem scale. The usefulness of chlorophyll fluorescence and remote sensing of chlorophyll fluorescence for largescale transpiration is still unclear. This study is an investtigation of physiological responses to environmental stimuli based on seasonal and diurnal observational measurements including micrometeorological data, gas exchange, chlorophyll fluorescence, and remotely sensed reflectance and derivative indices. We suggest means to estimate crown scale transpiration and WUE of a field grown oak tree Quercus lobata (valley oak) using spectral reflectance indices associated with chlorophyll fluorescence.

2. MATERIALS AND METHODS

2.1. Study Site

This experiment was conducted in Big Chico Creek Ecological Reserve (BCCER). BCCER is owned and managed by the Research Foundation of California State University, Chico for the purpose of preserving the critical natural habitat and providing environmental research and educational areas [19]. It is located in the foothills of the Sierra Nevada in the northern portion of the Sacramento Valley about 10 miles northeast of Chico, California, USA. The Reserve ranges in elevation from 213 to 623 feet, with mean precipitation ranging from 64 cm in the valley to 203 cm in the headwater region with hot dry summers and extended periods of limited rainfall. The Reserve includes 7.24 km of Big Chico Creek and encompasses 1599 ha of land. (Latitute 39˚51'51''N Longitude 121˚42'46''W).

2.2. Plant Material

A naturally occurring Quercus lobata (valley oak) with an open crown was selected for the study plant. This oak tree was found in a riparian area, which was located in the canyon of Big Chico Creek, and about 30 m from the creek (Figure 1). The tree had about 90 cm DBH and approximately 20 m height. The measurements including gas exchange, chlorophyll fluorescence and spectral reflectance were conducted on the southwest portion of the crown utilizing a constructed tower at approximately 15 m height.

2.3. Meteorological Measurements

A micrometeorological station was established about 40 m south of the study tree and 30 m from Big Chico Creek for the purpose of monitoring micro meteorological data including solar irradiance, air temperature and relative humidity at 2 m height from the ground (Figure 1). A quantum sensor [20] was mounted on the micro meteorological station. This sensor read global radiation (W∙m⁻²). The station was equipped with a humidity probe (HMP35A, Waisala Inc., Helsinki, Finland). The probe read relative humidity in percent and a thermistor (UUT51J1, Fenwal Electronics, Toledo, Ohio) was used to observe air temperature in degree Celsius. To utilize the recorded data for this research, specific data (from

Figure 1. Experimental site at big Chico Creek Ecological Reserve, CA, USA with micrometeorological station, study tower, study tree Quercus lobata (valley oak).

10:30 to 16:30 of experiment days) were read out to Microsoft Excel spread sheets. Time data of gas exchange, chlorophyll fluorescence and spectral reflectance measurements discussed below were modified to match with time data of weather data.

2.4. Gas Exchange and Chlorophyll Fluorescence Measurements

Leaf level gas exchange and chlorophyll fluorescence were measured using LI-COR 6400 infra-red gas analyzer (IRGA) [14] equipped with a leaf chamber fluorometer [20]. Diurnal cycles of gas exchange and chlorophyll fluorescence were taken from 10:30 to 16:30 on September 29^th, October 6^th, 7^th, 28^th and November 4^th. Intact leaves of the top crown were selected at the beginning of the measurements each day. These leaves were equilibrated prior to measurements. The LI-COR 6400 was programmed to take a sequence of measurements at 20 minute intervals of gas exchange and chlorophyll fluorescence. Gas exchange measurements included carbon assimilation rate (A, µmol CO₂ m⁻²∙s⁻¹), stomatal conductance (g_s), (µmol CO₂ m⁻²∙s⁻¹), transpiration rate (E, µmol H₂O m⁻²∙s⁻¹), substomatal CO₂ concentration (Ci, µmol CO₂ m⁻²∙s⁻¹), leaf temperature (˚C) and PAR (µmole photon s⁻¹∙m⁻²).

The chlorophyll fluorescence parameters of steadystate fluorescence (F_s), quantum yield and max quantum yield [21] (Maxwell and Johnson, 2000) were measured along with photochemical quenching (qP), and the fraction of the allocation of absorbed light energy by PSII antenna; and [22].

2.5. Reflectance Measurements

Crown reflectance of Q. lobata was taken with a portable spectrometer (UniSpec-SC, PP Systems, Haverhill, MA). A 2.1 mm diameter fiber optic with an approximate field of view (FOV) of 20˚ was mounted to a measurement station at 1 m height from the top of the crown at the nadir angle of 90˚. The measurement FOV at crownheight was 0.977 m².

The reflectance measurements were taken simultaneously with gas exchange and chlorophyll fluorescence measurements (every 20 minutes between 10:30 to 16:30). Between the measurements, Unispec-SC was calibrated using a reflectance-standard panel (Spectralon, Labsphere, New Hampshire) and the dark current was corrected for each measurement. Five readings (each composed of 40) scans of reflectance were taken and then averaged. The data were processed with software, Multispec, that interpolated from the original waveband (3.3 nm) of Uni-Spec-SC to 1 nm intervals to yield crown reflectance and derivative spectra and to calculate reflectance and derivative indices.

Reflectance indices used in this study were photochemical reflectance index (PRI) formulated as (R531 − R570)/(R531 + R570) [23], fluorescence ratio indices R690/R600 and R740/R800 [13], curvature index formulated as (R675 × R690)/R683² [14]. Derivative indices included double-peak index (Dpi) formulated as (D688 × D710)/D692 [17] and derivative chlorophyll indices formulated as D730/D706, D705/D722 and (D705 − D703)/D707 [10].

2.6. Data Analysis

Quadratic regression using a second order polynomial was used to determine relationship between two different measurement variables in this study. Calculated relation values (r² and p-value) were used to predict a Y-value from an X-value. The data were analyzed with polynomial regression function in scientific data analysis software (SigmaPlot ver. 10.0, Systat Software Inc., San Jose, CA).

3. RESULTS

3.1. Micrometeorological Data

Micrometeorological data revealed a typical pattern of seasonal diurnal cycle. Solar irradiance generally showed a gradual increase before noon and reached its peak between noon and 13:00 (Figure 2(a)). It started decreasing around 14:00 to 15:00. A sudden reduction was observed around 16:00 due to the landscape of the canyon blocking the sunlight shading the quantum sensor. Over the experimental period, a mean solar irradiance (mean ± standard error) dropped from 921.4 ± 46.9 W∙m⁻² (September 29^th) to 567.1 ± 54.3 W∙m⁻² (November 4^th). The lowest mean and highest global radiations were observed on October 28^th (562.2 ± 46.0 W∙m⁻²) and September 29^th, respectively.

Air temperature and relative humidity followed the diurnal cycle of solar irradiance (Figures 2(b) and (c)). However, the daily mean air temperature slightly increased from 20.4˚C ± 0.5˚C on September 29^th to 23.9˚C ± 1.1˚C November 4^th. The daily mean relative humidity didn’t change (28.8% ± 1.9% to 28.3% ± 3.5%). The highest mean air temperature (25.8˚C ± 0.6˚C) and relative humidity (34.6% ± 2.0%) were observed on October 28^th.

3.2. Diurnal Changes in Leaf Gas Exchange and Chlorophyll Fluorescence

The mean PAR changed from September 29^th (1599.1 67.8 µmole photon s⁻¹∙ m⁻²) to November 4^th (1369.6 ± 20.4 µmole photon s⁻¹∙ m⁻²) (Figure 3). The lowest mean PAR was observed on November 28^th (1231.2 ± 24 µmole photon s⁻¹∙ m⁻²) and September 29^th, respectively.

(a)(b)(c)(d)

Figure 2. (a) Time course measurement of the solar irradiance. Global radiation (W∙m⁻²) was measured on September 29^th, October 6^th, 7^th, 28^th and November 4^th. The data point on the graph starts at 10:30 on September 29^th and moves to the next data at 15 minutes interval until it reaches 16:30. The other 4 measurement days repeats same graphing pattern. A daily cycle completes with 25 measurement data point. Therefore, September 29^th completes its diurnal cycle from Time course number 1 to 25. Time course number 26 to 50, 51 to 75, 76 to 100 and 101 to 125 represents October 6^th, 7^th, 28^th and November 4^th; (b) Air temperature (˚C); (c) Relative humidity (%); (d) Leaf temperature (˚C).

(a)(b)(c)(d)

Figure 3. (a) Time course measurement of PAR (b) carbon assimilation rate (A) (c) stomatal conductance (g_s) (d) transpiration rate (E) monitored with the LI-COR 6400-40 leaf chamber. PAR (μmole photon s⁻¹∙m⁻²) was measured on September 29^th, October 6^th, 7^th, 28^th and November 4^th. Graphs are drawn with the same scheme used in Figure 2.

Leaf temperature was significantly related to air temperature (r² = 0.796, p < 0.001), but had a higher mean temperature than air temperature throughout the measurement (4.5˚C higher than air temperature) (Figure 2(d)).

Figure 4 shows time course measurements of WUE and LUE. Figure 5 show regression analysis of PAR with light use efficiency (LUE), chlorophyll fluorescence parameters and the allocation of absorbed light energy by PSII antenna. LUE and chlorophyll fluorescence parameters (, ΦPSII and qP) had a significant negative correlation with PAR (r² = 0.561 for LUE, 0.648 for, 0.730 for ΦPSII and 0.699 for qP). The allocation of absorbed light energy by PSII antenna showed different responses (Figure 5(c)). Heat dissipation indicated by 1 − hada positive correlation with PAR (r² = 0.648). or light energy that was not converted to photochemical energy thermally dissipated increased between 1000 and 1500 µmol photon m⁻²∙s⁻¹ of PAR and then stopped increasing its allocation at PAR greater than 1500 µmol photon m⁻²∙s⁻¹ (r² = 0.648). F_s decreased from 20˚C to 28˚C and then stopped

Conflicts of Interest

The authors declare no conflicts of interest.

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